Open calls in doctoral studies

Supervisor: Imre Taal

The littoral fish communities have received relatively little attention in ichthyology and marine sciences. Namely, the species in the area do not have direct significant economic value. However, littoral serves as an important spawning and feeding ground for many species, often determining the reproductive success of fish. Unfortunately, anthropogenic factors affect the area quite extensively, extending, for example, through the food web beyond the littoral zone. One such aspect is the presence of non-indigenous species. In 2020, a new littoral fish species was found in the Gulf of Finland. The Ponto-Caspian origin eastern tubenose goby (Proterorhinus semipellucidus (Kessler, 1877)) has a rapid dispersal ability and flexible food and habitat selection. A similar introduction occurred in the 2010s when the round goby (Neogobius melanostomus (Pallas, 1814)) reached the Gulf of Finland. Today, it has become apparent that the rapid increase in the abundance of round goby has been one of the most important factors affecting the structure of coastal sea fish communities in recent decades. While the impact of the round goby in the Gulf of Finland and the Baltic Sea in general has been extensively studied, the impact of the eastern tubenose goby is still largely hypothetical. This doctoral thesis aims to determine the probable extent of the eastern tubenose goby's spread in the Gulf of Finland and its role in food competition. In addition, the impact of nonindigenous species on the local littoral fish communities is analysed, comparing datasets from before the invasion of the round goby (2008), before the invasion of the eastern tubenose goby (2015), and after the invasion of both species. Since the eastern tubenose goby and the round goby occupy slightly different niches, their combined impact on the local ecosystem may become even more devastating, highlighting the importance of further investigation. While non-indigenous species studies typically begin after the establishment of a new species, this work also covers the pre-invasion period. Therefore, it represents unique research in the Baltic Sea region.

Supervisor: Jonne Kotta

This thesis addresses the challenges of assessing blue economy impacts in the Baltic Sea by combining in situ experiments, large-scale habitat mapping, and spatial-dynamic modeling to evaluate ecosystem responses under different management scenarios. A data-driven algorithm will quantify the cumulative impacts of blue bioeconomy activities and their interaction with existing stressors. Findings will be published in peer-reviewed journals, focusing on artificial substrate colonization, the ecological footprint of blue economy installations, and cumulative environmental impacts of renewable energy and aquaculture in the NE Baltic Sea.

Supervisors:Anu Albert, Lauri Saks & Markus Vetemaa

Within the framework of the doctoral project: 1. A review article will be prepared based on the results of Great Cormorant diet studies conducted in the Baltic Sea area; 2. The composition of the diet and the selectivity of the diet of a cormorant colony located in an important fish spawning area will be determined; the diet samples will be analysed in comparison with the results of fish monitoring results; 3. Changes in the diet of cormorants and changes in fish communities in the West Estonian Archipelago Sea over the past twenty years will be studies; 4. The impact of the colony located in the spawning area on the local fisheries will be assessed; 5. The relationship between the diet of cormorants and the small- and large-scale movements of individuals will be studied. The composition of the diet will be based on prey DNA determined from the pellets and droppings; the results will be associated with the movements of the individual birds, which will be studied using radio telemetry.

Supervisors: Georg Martin, Kristjan Herkül

Biodiversity plays a crucial role in maintaining ecosystem processes within changing environments. However, human activities have led to a decline in marine biodiversity globally. To combat this decline, it is essential to understand how biodiversity patterns and ecosystem functions are mechanistically and spatially linked to environmental factors, including human pressures. Current research primarily focuses on location-specific studies, while natural biodiversity patterns operate on much larger, continuous spatial scales. Mapping these large-scale biodiversity patterns is cost-prohibitive, necessitating alternative sampling models and research strategies to facilitate broader ecological investigations. This approach will enable researchers to address biodiversity-related questions across various spatial scales. Main Research Questions:

• Is the relationship between environmental variables and benthic biodiversity scale-specific?

• Can key spatial scales be identified where variability in benthic diversity and environmental impacts are most pronounced?

• Do consumer patterns align with those of primary producers?

• Do α-, β-, and γ-diversity of primary producers and consumers exhibit similar spatial patterns?

• What relationships exist between biodiversity, environmental niche space, and community biomass?

The study will utilize existing data from the benthic database of the Estonian Marine Institute. Additional biological sampling will involve underwater video recordings, still imagery, and quantitative biomass sampling (using SCUBA and grab samplers). Spatially stratified sampling designs will cover various spatial scales, and the field data will be analyzed with georeferenced environmental variable layers using statistical and machine learning techniques to uncover diversity patterns and their relationships with scale-dependent environmental drivers.

The research is expected to yield at least three papers in high-ranked scientific journals, addressing:

• Scale-dependent effects of environmental variability on coastal benthic biodiversity in brackish waters of the Baltic Sea.

• Relationships between spatial patterns of biodiversity, community biomass, and productivity in coastal ecosystems.

• Spatial relationships between the α-, β-, and γ-diversity of benthic producers and consumers in marine coastal environments and their effects on marine ecosystem functioning.

Supervisors: Tuul Sepp, Richard Meitern, Krista Fischer

Improved understanding of the factors that underlie the decline, low health status and risk of injuries for wild species in urban environments is critical to supporting the conservation of diverse biotic communities in cities. Small mammals are key components of terrestrial ecosystems with roles in seed dispersal, the regulation of both plant and insect prey and predator populations. The PhD project focuses on two keystone species for urban ecosystems: the common hedgehog (Erinaceus europaeus) and red squirrel (Sciurus vulgaris). Both species are common and beloved residents of urban gardens and can be called the ambassadors of wild animals, creating opportunities for using volunteer help and citizen science approach for studying these species, but also for awareness raising for urban ecology. In the last decade, hedgehog and squirrel numbers have dropped sharply in several European countries. Declines in both species likely occur because of a combination of factors, including habitat loss and fragmentation, environmental pollution, the use of rodenticides and other pesticides, road traffic, climate change and other threats. The reasons for the decrease are complex, but there is no systematic survey data. The PhD project will aim to create a database of injured and orphaned wildlife using Estonian Wildlife Rescue Centre data, and to develop a monitoring program (including banding program for both species) to estimate the reasons for death and injury of Estonian hedgehogs and squirrels.

Supervisors: Urmas Saarma, Ants Tull

Zoonotic pathogens form a so-called pathobiome that is a set of pathogenic organisms (prokaryotes, eukaryotes, and viruses), causing reduced health status of the host. The pathobiome studies are crucial, because attempts to explain disease conditions by identifying a single pathogenic organism are often incomplete. Since the health of humans, domestic and wild animals, plants, and the wider environment are closely linked and interdependent, including wild and domesticated animals into pathobiome studies is crucial, as many pathogenic organisms that infect animals, can also infect humans. Dogs are known to have a close relationship with humans. However, dogs carry many zoonotic pathogens that can lead to a myriad of diseases. The wolf (Canis lupus) is the wild ancestral species of dogs and wolves are capable of carrying a similar set of pathogens. Here, our aim is to perform complex analysis of the gastrointestinal organisms present in scat, both pathogenic and nonpathogenic. These include the whole communities of eukaryotes (helminths, protozoa, fungi), procaryotes (bacteria, archaea), viruses and bacteriophages. We also aim to study intra- and interspecific interactions of pathogenic and non-pathogenic organisms, which is crucial to understand the dynamics of gastrointestinal organism. For this, we collect fresh scat samples from dogs and wolves, but use also analyse ancient scats of dogs and wolves (so-called coprolites). Metabarcoding and metagenomic analyses will be used to reveal the communities of procaryotes, eukaryotes and viruses.

Supervisor: Tiit Teder

The future of biodiversity hinges on species' ability to adapt to climate warming. Thermal traits – characteristics that define how organisms interact with their thermal environment – are key determinants of a species’ capacity to cope with temperature changes. This PhD project aims to evaluate the evolvability and evolutionary rates of thermal traits in insects. A substantial body of empirical research, accumulated on thermal traits of multiple species, provides a solid foundation for broader generalizations and facilitates the investigation of cross-species variation through a series of meta-analytic studies in a phylogenetically explicit framework. This includes analyzing genetic differences in thermal traits across populations, comparing thermal responses among related species, and investigating sex-based variation in thermal traits. By synthesizing data from numerous studies, we aim to uncover patterns of thermal adaptation and improve our understanding of how insects may cope with climate change.

Supervisors: Ivika Ostonen-Märtin, Priit Kupper, Marili Sell

Plant roots and the associated mycorrhizal fungi play an important role in soil organic matter transport and stabilisation. Roots can grow throughout the year but due to methodological challenges in studying root growth dynamics, many aspects (such as growth peaks and species-specific traits) remain unknown. Moreover, continuous root and hyphae growth dynamics across different seasons in forests, particularly along nutrient and soil moisture gradients, remain largely unexplored. Recent technological advances (scanners underground, automated minirhizotron cameras) enable us to monitor detailed temporal and spatial root and hyphae growth dynamics, development, senescence, architecture and estimate the turnover rate. Image-based indicators necessary for machine-learning to distinguish between different plant species or to estimate root lifespan are yet to be developed. There is also a lack of knowledge about the relationship between the growth dynamics of roots and fungal hyphae and other ecophysiological parameters of plants. Understanding these dynamics is crucial, since soil water availability has a significant effect on the belowground biomass growth and soil nutrient cycling. Extreme weather conditions, including the prolonged and more frequent heatwave events significantly reduce soil water availability, impacting both the aboveground and belowground parts of trees. The hyphae connected to tree roots can access water from smaller pores and at greater distances, possibly help trees cope with water stress. To elucidate the climate change mediated alterations in forest water flux affecting belowground functioning, this project will combine several tree aboveground physiological measurements (continuous tree stem increment and water flux measurements) and belowground data (root growth, senescence, fungal hyphae growth, etc)

Supervisors: John Davison, Tanel Vahter, Martti Vasar

Soil harbours the majority of biodiversity on earth, and soil microbes are known to perform critical ecosystem functions. However, research on microbial communities often lacks detailed functional insights beyond taxonomy and broad guild membership. The characteristics of low-level microbial taxa, including their realised niches, remain poorly understood. This PhD project aims to estimate the realized niches of fungal taxa across multiple guilds, including mycorrhizal fungi, saprotrophs, and pathogens, Using global fungal occurrence data, the research will use hypervolume modelling to classify fungal taxa as bioindicators (using niche optima), and as generalists or specialists (using niche volumes), and to make inferences about community assembly mechanisms (using estimates of niche differentiation between taxa). Spatial and temporal variation in communities of fungi, prokaryotes and soil animals will be assessed using a high-resolution data set from Estonian ecosystems. Historical land-use data and metacommunity theory will be used to investigate extinction debt and rescue effects in soil microbial communities. Additionally, the project will evaluate the ecosystem-level effects of microbial community shifts under dynamic environmental conditions. Experimental work using Estonian soil microbial communities will assess ecosystem responses to environmental change, linking key ecosystem processes with microbial taxa and consortia, and their niche characteristics. This project will advance ecological theory and inform applied strategies for managing soil biota and ecosystems.

Supervisors: Inga Hiiesalu, Sten Anslan

While soil biodiversity is key to ecosystem stability, the interactions between microbes and soil fauna remain poorly understood, which limits our knowledge of how these interactions affect soil functions. This project investigates how microarthropod biodiversity shapes microbial communities and vice versa, focusing on top-down (predation) and bottom-up (resource availability) controls. It also examines the influence of abiotic factors and land-use intensity on these interactions. The PhD project has three main objectives: (1) developing a DNA-based metabarcoding workflow for soil microarthropods to reduce reliance on labor-intensive traditional methods, (2) analyzing microarthropod-microbial interactions through controlled mesocosm experiments, and (3) assessing how land-use intensity affects soil arthropod diversity across a gradient from grasslands to conventionally farmed fields in Estonia. Experiments will be conducted in mesocosms at sites with varying disturbance levels, using defaunated soil and reintroduced microarthropods. Soil biota will be analyzed using DNA metabarcoding and microscopy, while microbial biomass will be estimated via phospholipid fatty acid analysis. This research will integrate ecological, molecular, and taxonomical approaches to provide novel insights into soil biodiversity drivers and interactions. Its findings will contribute to sustainable land management and support the EU’s “Soil Deal for Europe.”

Supervisors: Leho Tedersoo, Saad Alkahtani

This project aims to disentangle the biotic and abiotic factors driving desert microbiome, using state-of-the-art molecular, bioinformatics and statistical methods. The purpose is also to understand how planting of semidesert trees may benefit from the local and inoculated microbiome. In all, the project seeks for ways how to ameliorate climate and land use change impacts on desert ecosystems.

Supervisors: Triin Kaasiku, Riinu Rannap

Conservation requires difficult prioritisation, especially for declining edge populations. The Baltic population of the southern dunlin (Calidris alpina schinzii) has declined from 1,400 to 450 breeding pairs in 25 years, with half nesting in Estonian wetlands. However, key knowledge gaps remain, particularly regarding population size and habitat viability. This thesis examines whether conservation efforts for this population are justified by assessing past conservation actions, evaluating habitat restoration effectiveness, determining the viability of mire-breeding populations, and comparing coastal grasslands and mires as breeding habitats. To understand the causes of decline, this research compiles historical and contemporary data on population trends, habitat conditions, and past conservation efforts across the species’ range. This evaluation will determine whether previous measures have been effective or if alternative strategies are needed. A key focus is coastal grassland restoration, where over two decades of monitoring and remote sensing data will reveal whether these habitats function as stable breeding grounds or ecological traps. Mires, which host a small but seemingly stable population, will also be assessed to determine if they serve as a viable refuge or a marginal habitat. Population size estimates, habitat use, food availability, and breeding success will be analysed, with colour-marking providing insights into survival and movement. If mires offer long-term stability, conservation should prioritise their protection; if not, resources may be better allocated elsewhere. A direct comparison of breeding success, habitat selection, and food availability between coastal grasslands and mires will clarify which habitat supports long-term population persistence. Climate models and predictive habitat assessments will further inform future viability. If one habitat proves superior, conservation should focus on it; if neither ensures survival, the feasibility of intervention must be reconsidered. By using the Baltic dunlin as a model, this research will provide critical insights into the conservation of declining edge populations, helping to guide future management decisions.

Supervisors: Toomas Esperk, Toomas Tammaru

Climate change poses a major challenge, yet the adaptability of species to shifting temperatures remains poorly understood. Thermal traits, which define an organism’s interaction with temperature, are often assumed to evolve slowly, though empirical evidence is surprisingly limited. Within a broader research programme on the evolution of thermal traits in insects, the proposed PhD project will collect experimental data for ~200 moth species of moths for a phylogenetic comparative analysis of thermal traits. This research is unique in its ambition to produce comparable cross-species empirical data on such a large scale. It will focus on (1) phylogenetic analysis of thermal limits in moth larvae, recorded in experiments on newly hatched individuals; (2) geographic variation in larval thermal limits across a European latitudinal gradient, informing shorter-term evolutionary rates; (3) phylogenetic conservatism of adult moth thermal limits, assessed through heat knockdown and chill coma recovery times; and (4) comparisons of thermal traits across conspecific moth populations. These studies will provide novel insights into the evolution of thermal adaptation across life stages and populations.

Supervisors: Liis Siinor, Heigo Ers

The development of new energy devices relies on novel electrolytes and materials. Meanwhile, the testing of an immense number of electrode-electrolyte systems is unviable. Thus, the adsorption and charge-transfer processes need to be studied in detail to develop a fundamental understanding of electrolyte selection. Knowing the electrical double layer properties allows the systematic design of interfacial properties for a given application. This doctoral project focuses on modifying the electrodeionic liquid interfacial properties through porphyrin adlayers on the electrode’s surface. The aim is to characterise physicochemical properties of various porphyrin adlayers at selected electrode – ionic liquid interfaces. The goal of studying these systems is to determine the impact of porphyrin adlayers on interfacial characteristics and evaluate the reversibility of metal deposition and formed surface structures. These insights pave the way to novel energy storage systems by increasing the compatibility of IL-based electrolytes with electrode materials, hindering the degradation of the electrode surface or the electrolyte, and developing a fundamental understanding guiding the selection of electrodes and electrolytes for selected applications.

Supervisors: Ester Oras, Koit Herodes

The project sets out to significantly improve the analytical resolution of organic residue analysis form archaeological pottery, allowing to a) refine temporal use phases of ancient ceramics for the first time, and b) enhance the detection and identification efficiency of different food sources in complex mixtures. Using both experimentally produced and archaeological pottery, the student will employ multi-instrumental mass-spectrometry methods in non-targeted and targeted modes (GC-MS (also in SIM mode), LC-MS), and compound specific stable isotope measurements (GC-C-IRMS) to identify different food components in complex food mixtures. The aim is to develop dedicated microsampling techniques for archaeological ceramics allowing the analysis of minimal sample amounts, and enhance various massspectrometry methods allowing to detect different food components, also in low concentrations. These methodological developments will contribute to the refinement of temporal use phases of ancient ceramics and improve the detection and identification of different food sources, leading to considerable methodological advancements in organic residue analysis from archaeological and paleoenvironmental samples.

Supervisors: Kaido Viht (PhD), Erki Enkvist (PhD)

Protein kinases (PKs) are enzymes involved in all aspects of a cell’s life. Excessive and unregulated activity of several PKs is associated with severe complex diseases, such as cancer. PKs are important drug targets and potential biomarkers of such diseases. The aim of the doctoral project is the development of novel long-lifetime photoluminescent (LLPL) probes for protein kinases, which could serve as research devices and contribute to the development of diagnostic methods. Target-responsive LLPL is a phenomenon that was first described by our group in 2011. LLPL probes of protein kinases emit long-lifetime photoluminescence upon binding to the catalytic subunits of PKs. When not bound, the LLPL signal from the probes is negligible. As a result, LLPL probes have several advantages over conventional fluorescent probes, including a better signal-to-noise ratio, higher selectivity of the LLPL signal, and possibility of being used in much higher concentration than the target PK in a mix-andmeasure assay format. Little is known about the structure-activity relationship of target-induced LLPL. This doctoral project focuses on developing LLPL probes with high affinity and selectivity for binding to PKs of the PIM family, PKN3, and CK2. Novel assay platforms will be created for the analysis of these PKs in homogeneous solutions and in cells. The investigation of novel compounds will help to improve the understanding of the structure-activity relationship of target-responsive LLPL.

Supervisors: Koit Herodes, Ivari Kaljurand

The shift from fossil-based chemistry to local and renewable biomass valorisation, like low-value wood, is driven by environmental concerns. This transition benefits local economies by enhancing the value chain and competitiveness of enterprises. Traditional wood valorisation methods, like the Kraft process and pyrolysis, are energy-intensive and environmentally harmful. The Tem-TA 85 project is focussed on wood valorisation and aims to fractionate lignin-cellulose mixtures into hydrolysis lignin and C6 sugars, leveraging their synergistic properties to create new materials such as polymers, nanomaterials, adhesives, and foams. Effective biorefining requires on-line chemical analysis, which is currently challenging due to the variability in biomass composition and long analysis times. To address this, the PhD project proposes using near-infrared (NIR) and Raman spectroscopy for rapid, on-line analysis. These methods offer a faster alternative to traditional off-line techniques like HPLC and NMR. The project will develop and implement these spectroscopic methods, enhancing the efficiency of modern biorefineries. The project's importance lies in its potential to advance wood valorisation and boost the competitiveness of Estonian companies. The plan includes developing and validating NIR/Raman methods through extensive calibration using off-line methods.

Supervisors: Kaido Tammeveski, Jaana Lilloja

Low-temperature anion-exchange membrane fuel cells (AEMFCs), operating at 60-80°C, are a promising technology for decarbonising the transportation sector, as they enable the use of nonprecious metal catalysts (NPMCs), offering a more sustainable and cost-effective alternative to already commercialised proton-exchange membrane fuel cells. Performance and stability improvements in AEMFCs can be achieved by increasing the operating temperature to >100°C. This doctoral research aims to develop novel biomass-derived, cost-efficient and sustainable oxygen reduction reaction (ORR) electrocatalysts with tailored porous structures for both lowand high-temperature AEMFC applications. The study will systematically investigate how varying pore sizes affect the AEMFC performance and examine the impact of dopants (Fe, N, S, P) and synthesis conditions on ORR electrocatalytic activity and stability, especially at elevated temperatures.

Supervisor: Nadežda Kongi

Methanol plays a crucial role as both a fuel and a chemical feedstock, but conventional production relies heavily on fossil fuels, leading to significant carbon emissions. Electrochemical CO2 reduction (eCO2R) presents a promising sustainable alternative by transforming captured CO2 into methanol using renewable electricity. This method has the potential to lower emissions and create a more circular carbon economy. However, technical challenges such as low efficiency, poor selectivity, and competing side reactions currently limit its large-scale application. This PhD project is dedicated to advancing eCO2R technology by focusing on three key areas: catalyst synthesis, electrochemical process optimization, and reactor design. By developing highly efficient and selective catalysts, optimizing reaction conditions, and designing advanced electrochemical system, we aim to enhance methanol production while reducing energy costs and improving overall sustainability. As a result, a prototype reactor will be designed, built, and tested to evaluate its performance under real-world conditions. The goal is to develop a scalable, high-efficiency system that can contribute to reducing industrial CO2 emissions and integrating renewable energy sources into chemical manufacturing.

Supervisors: Signe Vahur, Anu Teearu-Ojakäär, Ivo Leito

The knowledge about the composition of the materials used for cultural heritage objects is important for history (elucidating the origin, age, authenticity of the artefact) and conservation (for choosing suitable conservation materials). Cultural heritage materials (e.g. paints, varnishes, glues, etc.) have a very complex multi-component composition, are aged and determination of organic materials with common chromatographic and mass spectrometric (MS) techniques (require sample piece, dissolving the sample, specific sample preparation and measurement conditions) is challenging. In addition, for the analysis of cultural heritage objects, non-destructive and direct surface analysis methods are preferred (however, most of them do not give full information about components in organic materials).

Under the chair of analytical chemistry, the cultural heritage research group is developing a novel laserbased MS device enabling it to perform analysis under atmospheric conditions, directly on the object of the solid material surface, without the removal of sample piece and specific sample preparation.

The main objective of this PhD project is to develop measurement methodologies for novel laser ablation (LA) atmospheric pressure chemical ionisation MS (LA-APCI-MS) method for the analysis of various cultural heritage materials. Additionally, the aim is also to investigate the destructiveness of laser ablation and APCI ionisation processes in the analysis of solid materials and give an evaluation of the capabilities of the LA-APCI-MS technique

Supervisors: Heiki Erikson, Kaido Tammeveski

Green politics and advancements in technology has increased the demand for clean hydrogen and thus researchers have turned attention to develop more efficient hydrogen production technologies. The aim of this project is to develop and improve the catalysts for anion exchange membrane water electrolysers. Main focus is to improve the anode reaction, which is the sluggish oxygen evolution reaction. Various nickel and iron-based catalysts are prepared during this project to maximise the number of active centres, improve electrocatalytic activity and stability so that these materials could be used in the anion exchange membrane water electrolysers.

Supervisors: Marek Mooste, Nadežda Kongi, Kaido Tammeveski

The global world has recently become more focused on sustainable development, driven by increased environmental concerns and rising energy consumption. This urgency has led a move to renewable energy sources such as solar, wind and hydropower, which are viewed as critical steps towards a greener and more sustainable future. However, the intermittent nature of these renewable sources makes it difficult to match energy supply with demand, especially in grid systems. This imbalance highlights the importance of effective electrochemical energy storage options. Among these, zinc-air batteries (ZABs) have garnered significant attention due to their potential for higher theoretical energy densities and inherent safety advantages.

Zinc-air batteries, which are a safer and more environmentally friendly alternative to lithium-ion batteries, make use of readily available zinc resources. Furthermore, they are attractive for a range of applications, from consumer electronics to electric vehicles and grid-scale energy storage systems, because of their lower cost and ecologically friendly qualities.

The research plan outlined in the proposed PhD project takes a comprehensive approach to revolutionise energy storage through the development of rechargeable zinc-air batteries. By integrating principles of green synthesis and utilising more sustainable catalysts, the research aims to enhance the stability, durability and cycle life of these batteries. Additionally, the project focuses on optimising electrolyte formulations and membrane materials to further improve performance and efficiency. These efforts are crucial in addressing the challenges that currently limit the widespread adoption of ZABs. Moreover, the project explores innovative concepts such as flow battery configuration, which could further expand the application of ZABs in diverse settings. Through these endeavours, the PhD project is poised to contribute significantly to the advancement of sustainable energy technologies driving the transition towards a more environmentally friendly and energy-efficient future.

Supervisors: Prof. Gunnar Nurk, Researcher Indrek Kivi

The aim of this project is to develop a stable surface layer for a ceramic La0.65Sr0.30Cr0.85Ni0.15O3- (LSCN) electrode and a metallic Ni current collector for a high-temperature electrolyser application. In order to improve stability, the concentration of oxide ion vacancies generated in the electrode surface layer during electrolysis as a result of cathodic polarization has to be reduced. The concentration of oxide ion vacancies can be reduced by doping the surface layer with cations with a stable oxidation state. A dopant layer (Al3+, Ga3+, Hf4+ or Mg2+) will be deposited onto the outer surface of the porous LSCN electrode by magnetron sputtering method and integrated into the surface layer of the mixed conductive electrode by thermal treatment. By changing the temperature programs distribution of the dopant in the surface layer is varied. The resulting surface layers and cross-sections of the surface layers are characterized by photoelectron spectroscopy (XPS), by time-of-flight mass spectrometry (TOFSIMS) and by X-ray diffraction (XRD) to identify the chemical and structural properties of the prepared surfaces and the dopant distribution in the LSCN surface layer. Short-term electrochemical tests identify electrodes with sufficient activity, which are then characterized electrochemically in a long-term test at different cathodic polarizations. After the long-term test, chemical and structural analysis will be carried out.

Supervisor: Arun Kumar Singh

Multirotor drones have emerged as one of the prominent robotics tools for mapping, search and rescue, inspection, cinematography and entertainment. Thus, there is a strong need to develop a robust autonomy stack for these drones. A fundamental challenge however, is that the quadrotor drones have a small form-factor and thus can carry only limited sensing and compute hardware. Even many ground robots prefer noisy depth sensors over Lidar to minimize energy usage.

The overall objective of the PhD project is to develop robust navigation algorithms for multirotors and other mobile robots that relies on only low-powered vision sensing and can run in real-time on low-power embedded devices such as Jetson TX2. The proposed PhD project aims to find a middle ground between classical and data-driven stack. In particular, the following core ideas will be explored: (i) learning vision-based world models through Gaussian splatting or Nerf. (ii) risk-aware planning over the learned models taking into account model uncertainty and (iii) endto-end reinforcement learning or imitation learning algorithms.

Supervisor: Arun Kumar Singh

Deep Neural Networks have revolutionized language modeling and computer vision. However, its impact in developing motion planners and controllers for robotic applications has been rather limited. The existing works struggle to make the transition from laboratory scale experiments to real-world deployability, especially in safety critical applications. The core issue is that planning and control policy represented purely in the form of deep neural networks invariably struggle to respect the constraints of the task (e.g vehicle stability on rough terrains, collision avoidance, etc). The lack of safety guarantees is glaringly evident even when the task or the environment variation is very small.

The core objective of this project is to improve the deployability of data-driven neural networks-based planners and controllers. The project will especially focus on use-cases (off-road mobility, crowd navigation, drone-based structure inspection) where large scale data collection is prohibitive and safety is critical. The former restriction essentially implies that the deployability bottleneck in these applications cannot be overcome by just scaling the data and network size.

The underlying hypothesis of this project is that data driven data end-to-end adaptation holds a lot of promise due to its flexibility. But to realize its full potential, some notion of safety and explainability need to be enforced into the neural policies by design. The project aims to achieve this by embedding structured priors, derived from physics, optimization, control theory and classical search into neural network pipelines.

Supervisor: Huber Raul Flores Macario

Exercise is essential for well-being, and contact sports promote long-term physical activity. However, their intensity can lead to health risks, including muscle damage and cognitive issues from concussions. Existing wearable devices track general activity but fail to provide actionable insights for contact sports. This project designs wearables to specifically monitor health impacts from contact sports, tracking gradual deterioration during training. We also explore strategies to improve wearable adoption by integrating data from multiple devices. By developing context reasoners to provide richer insights, our approach enhances user engagement, offering real-time feedback to prevent health issues and optimize training.

Supervisors: Abbas Cheddad, Kallol Roy and Victor Henrique Cabral Pinheiro

This "Doctored Media Forensics" research proposal addresses the critical challenge of detecting visual forgeries in the digital age, focusing on developing an intelligent automatic system to combat deepfake technologies. As fake images and videos proliferate, potentially triggering societal unrest and political instability, this project aims to create practical research deliverables that enhance visual forensics capabilities. The methodology involves analysing statistical distortions in doctored media, examining generative adversarial network (GAN) architectures, and utilizing the Halide programming language for optimized implementation. Currently, few research groups are effectively addressing visual forgery detection, with existing methods lacking reliability and robustness. By extracting salient fingerprints from advanced generative models like StyleGAN, we may be able to create more sophisticated detection mechanisms that can identify artificially generated content. Our research promises significant societal benefits, including raising public awareness about digital misinformation, generating high-quality academic publications, and potentially integrating findings into the UT educational programs. With deepfake technologies becoming increasingly sophisticated, this project represents a crucial scientific response to the growing threat of digitally manipulated visual media, offering a robust approach to protecting digital information integrity and to restoring the lost trust in digital media.

Supervisors: Somnath Banerjee, Kallol Roy

Misinformation has been a longstanding and serious concern in the evolving landscape of generative AI. The rise of social media has significantly simplified content creation and sharing, which has, in turn, facilitated the rapid spread of fake web content and amplified its impact. Large Language Models (LLMs), such as ChatGPT and GPT-4, have demonstrated remarkable proficiency in natural language processing tasks, producing text that closely resembles human writing. While these models offer potential in detecting misinformation by leveraging their extensive knowledge bases, they also present risks. LLMs can be exploited to generate misleading content that is more convincing and harder to detect than humancrafted misinformation. This project aims to develop a mixture of expert (MoE) machine-learning techniques to mitigate misinformation. MoE consists of several specialized neural network models (experts), each specializing in different aspects of language (e.g., syntax, parts of speech, semantics) connected by a gating network. The gating network routes the input text to the appropriate expert subnetwork to solve the misinformation tasks. We propose to build a formal verifier expert that works in tandem with other statistical/neural experts in our proposed MoE architecture. The formal verifier systematically evaluates the logical structure of the text to identify and verify potential misinformation. By combining the strengths of statistical and formal verification methods, the ensemble aims to effectively detect and mitigate the spread of false information. The proposed MoE architecture offers a promising pathway to counteract these threats and uphold the integrity of information ecosystems. This dualfaceted strategy addresses both social and technical dimensions of the misinformation problem.

Supervisor: Marina Lepp

Recent advancements in AI have created possibilities for making education personalized and scalable that were impossible before. Modern learners already use Large Language Models (LLMs) such as ChatGPT, Gemini, or DeepSeek, to quickly clarify complex concepts and retrieve relevant information. However, these general-use tools tend to produce inaccuracies and “hallucinations”, risking learner confusion, and they lack the methodology to guide a student to the correct answer stepby-step. An effective AI educational tool should prioritize accuracy as well as educational instructional strategies rather than just answer delivery.

Retrieval-Augmented Generation (RAG) enables AI systems to integrate course-specific material with LLM capabilities, but aligning these technologies with pedagogical principles is still challenging. The proposed doctoral thesis would include building, integrating and evaluating a prototype of an AI Teaching Assistant (AITA), an LLM-based conversational interface that provides a personalized approach, context-aware feedback, and scaffolded learning experience. AITA's architecture will include RAG for curated answers, that use content from a real course or MOOC at the University of Tartu. The model will be prompted to guide and mentor students, rather than answering the question. Major attention will be paid to finding the best model that can provide high-quality answers in both English and Estonian.

The research steps include a literature review of LLMs, RAG methods, and educational prompt engineering, followed by a prototype implementation of one of the Computer Science courses or MOOCs. The evaluation, conducted through performance metrics, surveys, and interviews, aims to measure student learning improvements and teaching staff workload reduction. This doctoral thesis seeks to improve the quality of STEM education and establish effective practices in AI-driven teaching.

Supervisor: Hina Anwar

The growing demand for AI solutions comes with significant energy consumption challenges, yet existing tools provide limited support for assessing and optimizing energy efficiency during early-stage development. This Ph.D. thesis aims to develop ECOMod, a prediction and recommendation system designed to estimate the energy consumption of AI solutions at an early stage. By leveraging AI energy benchmark datasets, ECOMod will predict energy usage across various use cases and provide actionable recommendations for optimizing AI models to balance energy efficiency with runtime performance. To ensure real-world applicability, the system will be refined and specialized for mobility and healthcare domains. Additionally, this approach supports global sustainability goals and aligns with regulatory frameworks like the European Union’s AI Act, promoting responsible and energy-efficient AI development.

Supervisor: Helger Lipmaa

Lattice‐based cryptography has emerged as one of the most robust approaches to achieving post‐quantum security. This PhD project aims to construct novel zk‐SNARK schemes using lattice assumptions such as Short Integer Solution (SIS) Learning With Errors (LWE). By leveraging the hardness of lattice problems against both classical and quantum attackers, these protocols seek to safeguard the zero‐knowledge property well into the quantum era. The candidate will explore new polynomial commitment schemes and circuit arithmetizations that map efficiently onto lattice structures. Emphasis will be placed on optimizing proof size, prover runtime, and verifier complexity, while maintaining strong theoretical soundness. Through rigorous reductions and formal security proofs, these zk‐SNARKs will be tied to well‐studied lattice assumptions. Additionally, prototype implementations will measure real‐ world performance in various domains, from decentralized finance to privacy‐preserving data analytics. Early research tasks involve analyzing existing lattice‐based zero‐knowledge protocols and identifying key efficiency gaps. Subsequent work will focus on designing novel techniques that exploit special algebraic properties of lattices. The student will integrate these methods into a complete system that ensures succinct communication overhead and robust security. Ultimately, this project will establish a powerful framework for practical, lattice‐ based zk‐SNARKs that remain secure in the face of future quantum threats. The Ph.D. student and collaborators will publish in top cryptography and coding-theory venues.

Supervisor: Miika Juhani Hannula

Logic serves as a foundational tool in computer science, underpinning query languages like SQL and providing machine-independent insights into complexity classes through descriptive complexity theory. The proposed doctoral thesis aims to extend this foundational role by developing tools for computational and descriptive analysis of problems over diverse classes of semirings.

Semirings, such as the Boolean semiring and the tropical semiring, are today applied in database query evaluation. The idea is to annotate database facts with semiring values and propagate them through queries to indicate the confidence, cost, or provenance of query answers. For instance, the same query can output the transitive closure of a graph or the shortest path lengths between nodes, depending on the semiring chosen. Semiring semantics is a growing area of interest, particularly in database theory and computer science logic. This doctoral research will explore how principles of logic, databases, and computation extend from Boolean contexts to arbitrary semirings. Potential research directions include generalizing database normalization for annotated databases, developing descriptive complexity theory for semiring computation based on the Blum-Shub-Smale model of computation, and exploring finite model theory for logics with semiring semantics.

The student will benefit from preparatory courses at the University of Tartu and a planned six-month visit to the University of Helsinki for advanced studies in logic. Collaborative reading groups and research interactions will also enhance the student’s learning experience. The project will involve international partnerships in Finland, the UK, and Germany. The research aims to contribute significantly to the growing body of knowledge in semiring semantics, offering promising opportunities for exploration and innovation.

Supervisor: Vesal Vojdani

This Ph.D. project aims to enhance the reliability and usability of automated software verification tools by developing new algorithms for sound static program analysis, creating machine-checkable witnesses, and providing human-readable explanations of analysis results. The research will focus on explainable witnesses for data races, theoretical advancements in explainable abstract interpretation, and the continuous evaluation of verification algorithms to ensure their practical effectiveness.

Supervisor: Prof Kuldar Taveter

Human-centric Artificial Intelligence (AI) systems are AI systems that are aligned with human goals, context, and concerns. Importantly, human-centric AI systems should adhere to human values, the concept of which has been thoroughly researched in psychology and sociology. Since emotions by end users of AI indicate adherence to or violation of human values, an important part of the PhD project work will be concerned with designing empathic AI systems.

The existing approaches to designing human-centric AI systems are largely ad hoc and do not cater for the higher degree of uncertainty and risks associated with AI systems. It has been recently identified in the research literature that there is a lack of studies on requirements engineering (RE) for human-centric AI systems.

The main goal of this doctoral project proposal is to put forward a reusable framework for designing human-centric AI systems. To achieve this goal, we first plan to develop a taxonomy of human values for AI systems. We will then study human needs for AI with emulated emotions and explore and combine methods for recognizing emotions. Informed by the taxonomy of human values for AI systems, human needs for emotions to be emulated by AI, and methods for recognizing emotions, we will work out the framework for designing empathic AI systems compliant with human values that consists of the methods for the elicitation, modelling and validation of requirements for human values and emotions for AI systems.

Supervisors: Janno Siim, Helger Lipmaa

FRI‐based STARKs employ Interactive Oracle Proofs rooted in coding theory to achieve scalable, transparent zero‐knowledge proofs without a trusted setup. They leverage low‐degree testing through efficient “Fast Reed‐Solomon Interactive” procedures, enabling near‐linear proving time and fast verification. Recent research has introduced optimized FRI variants further to reduce prover and verifier complexities and proof sizes. Our Institute of Computer Science hosts a dedicated coding theory group, offering rich expertise in error‐correcting codes. By collaborating closely with them, this PhD student can adapt classical code families or devise new ones that optimize IOP protocols for STARK‐style proofs. Such synergies promise improved distance properties, faster interpolation, and novel proximity tests, all critical for robust zero‐knowledge statements. Empirical validation will include building prototype implementations to assess performance gains in blockchain and distributed ledger contexts. Ultimately, we aim to establish a future‐proof, quantum‐ resistant foundation for proof systems by harnessing modern code-based IOP frameworks. Success in this research will solidify a cutting‐edge collaboration bridging coding theory and cryptography, ensuring long‐term impact on STARK‐ based protocols. The Ph.D. student and collaborators will publish in top cryptography and coding-theory venues.

Supervisors: Amnir Hadachi, Salijona Dyrmishi

Urbanization has significantly increased road traffic emissions, deteriorating air quality, and urban livability. Addressing this challenge requires advanced monitoring, analysis, and mitigation strategies. Therefore, this PhD project explores the integration of Internet of Things (IoT) and Machine Learning (ML) technologies to enhance real-time emission monitoring, optimize traffic flow, and promote sustainable urban solutions. Additionally, the project investigates how these technologies can enhance carbon sequestration efforts through smart urban green infrastructure. By leveraging AI-driven automation, this research aims to contribute to sustainable smart cities with cleaner air, improved urban planning, and environmentally friendly mobility solutions.

Supervisor: Dmytro Fishman

This PhD project focuses on developing novel, efficient, and highly accurate instance-aware segmentation models for biomedical applications, particularly wholecell segmentation from microscopy images. Instance segmentation is essential for precisely identifying and separating individual objects in an image. In biomedical fields, such as cell segmentation, this precision is very important, as the number and properties of cells are critical indicators of sample health. Existing models like MaskRCNN and MaskDINO perform well with natural-world images, but struggle with biomedical imaging, where cells have heterogeneous shapes, blend with backgrounds, overlap, and sometimes exhibit partial transparency.

The proposed solution is a new instance segmentation architecture called Instance Aware U-Net (IAUnet). This architecture integrates instance awareness into the UNet model, using query-based mechanisms for accurate pixel-to-instance clustering. IAUnet is designed to handle overlapping cells and is both accurate and lightweight, making it practical for real-world use in biomedical imaging. To validate the model’s superiority, the project will release a custom dataset of overlapping cancer cells, collected and annotated specifically for this research.

The doctoral project will begin with finalizing the IAUnet paper, which is near submission. Upon acceptance, the custom dataset of overlapping cells will be made publicly available. The next phase will involve testing the model’s ability to detect and segment overlapping cell regions using both the custom dataset and publicly available datasets. Finally, the project will explore further optimizations to extend IAUnet’s applicability to other biomedical imaging tasks beyond cell segmentation.

Supervisors: Mubashar Iqbal, Raimundas Matulevičius

Smart infrastructures face numerous challenges, including managing and storing data from large-scale Internet of Things devices, enabling real-time communication, ensuring security, and protecting user privacy. These complexities highlight the need for integrating blockchain technology and AI into smart infrastructures. Thus, this project aims to establish a comprehensive framework for smart infrastructure security by leveraging blockchain and AI. Blockchain introduces a decentralised, distributed, and tamper-proof system that facilitates secure and transparent data sharing among various components and stakeholders. This integration enhances smart infrastructures' overall security, efficiency, and reliability. Additionally, AI will empower smart infrastructures for real-time anomaly detection, predictive maintenance, and intelligent decision-making to further strengthen smart infrastructure against potential security threats.

Supervisor: Victor Henrique Cabral Pinheiro

Reward Hacking (RH) is a phenomenon where reinforcement learning (RL) agents exploit imperfections in reward functions. This issue becomes critical in training large language models (LLMs) with Reinforcement Learning with Human Feedback (RLHF), as misaligned objectives can result in unexpected and dangerous behaviours. Sufficiently capable RL agents trained to get high rewards in a diverse set of environments could become general reward hackers. Undesired instances arise where machine learning models learn to modify unit tests to pass coding tasks or responses that contain biases to mimic a user’s preference are major blockers for real-world deployments. RH has profound societal and ethical implications, as misaligned Large Language Models (LLMS) can undermine trust in real-world safety-critical systems such as healthcare, autonomous systems, etc. Current approaches to mitigating RH focus on ad hoc solutions, such as redesigning specific reward functions or implementing logic-based constraints, which provide only temporary solutions. This project seeks to address the RH problem by introducing an information-theoretic framework for reward modelling that mitigates it during the training of LLM with RLHF. Using the concepts of entropy and mutual information from information theory, we propose to construct new reward models that incentivize RL agents to prioritize meaningful, generalizable behaviours over reward exploitation, thus assisting the LLMs to align with human intentions. The reward modelling is first formulated into a variational optimization problem of information bottleneck (IB) objective function. Optimizing IB gives control over the latent representation, which serves as a critical intermediary between model outputs and human preferences. This eliminates human preference-irrelevant (bias) information via IB latent representation. The other method we propose is to use smooth noise (perturbations) in RL agent reward functions. A small random perturbation (outside the RL agent's control) in the reward. RL agents are now unable to correlate smooth noise parameters (mean and variance) with rewards. This weakens the RL agents and discourages reward hacking behaviour.

Supervisor: Hedi Peterson

Advancements in artificial intelligence (AI) are transforming life sciences, particularly in image processing. This doctoral thesis aims to develop innovative AI-based tools and frameworks to tackle challenges in life science imaging, improving data analysis accuracy and efficiency. The research will provide scalable solutions for researchers working with complex imaging data, advancing our understanding of cellular and organismal behaviour.

The first objective involves further development of a web tool, FiBar, for measuring fibre diameters in scanning electron microscopy (SEM) images. Unlike current methods, FiBar already employs AI-driven algorithms to enhance measurement precision and adaptability.

The second objective focuses on refining a high-throughput intracellular model to integrate AI-based feature extraction techniques. This work will study the effects of bacterial infections on eukaryotic cell morphology and dynamics, addressing challenges beyond the capabilities of tools like CellProfiler by identifying subtle, often overlooked morphological changes.

Additionally, the thesis explores the cross-applicability of AI methods across cell types and species, with a particular emphasis on improving cell and/or other biological system tracking for longitudinal studies. Recognizing that many researchers lack tools or resources to analyze imaging data effectively, the research aims to develop targeted AI solutions to bridge these gaps.

This research focuses on the following problems: (1) further development and validation of FiBar, (2) refinement of the intracellular model, and (3) exploration of AI solutions for tracking and cross-applicability across cell types. Results will be shared through scientific publications, conferences, and open-source web tools, ensuring accessibility and maximizing impact within the life sciences community.

Supervisor: Dmytro Fishman

The PhD project aims to investigate accurate object classification through advanced segmentation models in medical imaging. Traditional object classification methods predict a single class label per image, such as identifying an image of a banana. While effective when image-level labels are available, this method struggles in medical imaging, where precision is crucial. A significant challenge is that such models cannot identify which specific pixels represent the object, leading to issues with background noise and overfitting, especially when data is limited and background noise is prevalent.

This project proposes an alternative approach using segmentation masks, which offer more detailed information about the object. By providing pixel-level insights, segmentation masks help models better distinguish relevant features from background noise. Previous studies have demonstrated the potential of segmentation-guided classification to improve model performance. Although pixel-level masks are more difficult to obtain, they significantly reduce overfitting and improve model accuracy, which is crucial for solving real-world medical challenges.

The project will start by finalizing x Master’s research, which focuses on using segmentation to classify cell cycles from microscopy images. This will be compared with traditional image-level classification methods and feature-based machine learning models. Future work will expand this approach to other medical imaging tasks, improving classification accuracy and robustness across various applications.

Supervisor: Dmytro Fishman

This PhD project aims to explore memory-efficient deep learning models for fullstack 3D medical image generation, specifically focusing on enhancing 3D microscopy images. Recent advancements in microscopy allow for detailed 3D imaging of biological components like cells, but challenges such as blurry images due to light penetration in deep structures remain. Classical methods have not been effective, while deep learning approaches have shown promise, including our prior work.

Here, we propose to use state-of-the-art generative models like GANs and diffusion models for deblurring 3D microscopy images. A key innovation will be optimizing these models for memory efficiency, enabling their use on standard hardware, which will make them accessible to a broader range of biologists and researchers.

The doctoral plan includes finalizing current work on GANs for deblurring, reducing model size and memory usage through the optical properties of samples, and conducting use-case studies to demonstrate the technology’s applicability in biological experiments.

Supervisor: Radwa El Shawi

The increasing prevalence of data-intensive applications has led to the emergence of streaming data as a critical domain in machine learning (ML). Unlike batch learning, streaming tasks require models to adapt continuously to evolving data distributions without the luxury of retraining on static datasets. Automated Machine Learning (AutoML) has revolutionized model selection and hyperparameter optimization in batch settings, but its application to streaming tasks remains a significant challenge. This proposal aims to develop novel AutoML techniques tailored for streaming environments, ensuring real-time adaptability, efficiency, and robustness in dynamic data scenarios.

Supervisor: Meelis Kull

As AI systems become more integrated into critical applications, ensuring the reliability of these hybrid systems is increasingly important. Traditional software engineering provides structured and predictable behavior, while machine learning enables flexibility and adaptability. Hybrid AI systems combine these strengths, but they also introduce new challenges. Neural networks can recognize complex patterns but struggle with unfamiliar inputs, sometimes producing incorrect yet confident responses. This unpredictability poses risks in high-stakes domains such as healthcare and automation, where errors can undermine trust and safety.

This research aims to improve the reliability of hybrid AI systems by developing methods to detect and manage uncertainty in machine learning components. By integrating neural networks with structured, verifiable software, the project seeks to ensure that AI operates within well-defined expertise boundaries. The goal is to enhance decision-making in hybrid systems, making them more robust, transparent, and trustworthy.

Supervisor: Huber Raul Flores Macario

Distributed Machine Learning (DML) enhances AI across decentralized systems with applications in environmental monitoring and drone surveillance. By federating training, methods like split and federated learning improve model performance, but data distribution creates security risks, especially from poisoned data. As DML expands into devices like autonomous vehicles, drones, and smart appliances, the risk of poisoning attacks increases, potentially disrupting critical functions and causing damage. Current poisoning detection methods are ineffective for large-scale DML applications involving diverse devices. Most solutions rely on cloud-based anomaly detection, which is impractical for decentralized systems, while XAI techniques are time-consuming and require physical recalls. Other methods analyzing local model updates need privileged access, making them difficult to implement. This project proposes a novel approach to detect poisoning attacks without server or model access by monitoring device I/O operations, which are affected by model performance and internal parameters. By analyzing performance changes over time and across devices, abnormal behavior can be identified, isolating affected devices. This method enhances DML security, essential for large-scale, security-critical devices like drones, autonomous vehicles, and IoT appliances, where poisoning attacks can have severe consequences.

Supervisors: Anna Aljanaki, Kallol Roy, Raivo Jaaniso, Heikki Junninen

The main goal of the project is to develop machine learning methods for digital technology, which, thanks to its non-invasive use, enables large-scale screening of chronic brain diseases and significantly increases their detection in the early presymptomatic stage. The technology is based on the analysis of volatile biomarkers of the disease with an electronic nose, the successful implementation of which requires a detailed mass spectrometric preliminary study and the development of new machine learning models. In this work, the mass spectrometric data are analysed to develop a methodology for taking breath samples, and machine learning models are developed for identifying biomarkers. By adding data from volatile compound sensors, an optimal machine learning model for the prototype of the electronic nose is developed.

Supervisors: Ebe Merilo, Hanna Hõrak, Liina Jakobson

Stomatal traits (pore aperture width, stomatal density and distribution between upper and lower leaf surfaces) are important for gas exchange between the leaf and the atmosphere and affect plant production, water loss and stress tolerance. Thus, changing stomatal traits may serve to breed plants with higher production in wellwatered versus drought-prone environments. This project aims to study stomatal traits of different monocot and dicot plant lines (barley and tomato) and their association with yield. During the project, leaf stomatal and physiological traits of OST1- deficient single and double mutant barley lines showing impaired stomatal regulation will be studied together with their yield to reveal the photosynthetic and yield potential of these lines. In addition, we will address the association between gene variants of stomatal developmental regulators and stomatal traits in tomato, and the effects of stomatal traits on yield production in this dicot crop. The project will result in an improved understanding of the role of different stomatal traits in plant physiology and their importance for fruit and grain yield in agronomically important mono- and dicot species.

Supervisor: Kuno Kasak

Rising CO2 and CH4 levels drive climate change, with peatlands, which are key carbon stores being at risk due to drainage for agriculture and forestry. This accelerates decomposition, turning them into carbon sources. Sustainable management requires balancing trade-offs in hydrology, vegetation, and soil processes, yet continuous eddy covariance data from managed peatlands remain scarce. This PhD project in Rumba, Estonia focuses on measuring greenhouse gas fluxes (CO2, CH4, and N2O) from drainage ditches, forest floors, and tree stems, with continuous eddy covariance monitoring. Ecosystem-scale experiments will assess water level regulation, selective harvesting, and clear-cutting impacts.

Water quality monitoring, including dissolved organic carbon measurements will help to explain carbon loss pathways. The goal of the project is to identify sustainable management strategies to enhance carbon sequestration. The main objectives of the PhD project are to: a) demonstrate novel sustainable peatland forest management practices; b) identify key environmental drivers regulating carbon sequestration and methane fluxes and; c) model and evaluate trade-offs and co-benefits of different management strategies.

Supervisors: Margit Kõiv-Vainik and Danielle Dagenais

In recent decades, due to the environmental problems associated with intensive urbanization and climate change, the implementation of nature-based solutions (NBS) for urban stormwater management has become increasingly important. Blue-green infrastructures (BGIs), such as rain gardens, swales, and constructed wetlands, are vegetated vertical flow systems designed to buffer runoff volumes on urban streets and to manage water collected by stormwater drainage systems. BGIs are highly adaptable to location-specific conditions and set goals. These solutions reduce urban flooding, help to treat stormwater, support biodiversity, and provide additional ecosystem services. The effectiveness of BGIs can be affected by initial design flaws, construction issues, inappropriate plant selection, ageing, and lack of maintenance. Cold climate conditions and four seasons add seasonal stress and specific pollutants that impact the plant communities of BGIs and, thus, their overall performance and appearance. The deterioration of these infrastructures can cause dissatisfaction among local residents and other stakeholders. Vegetation is crucial for the technical functioning, efficiency, aesthetic appeal, and other ecosystem services of BGIs.

The long-term survival and natural succession of plants in BGIs have been little studied. This doctoral thesis aims to determine the plant succession in long-term stormwater solutions and to identify factors in planning, implementation, and maintenance that affect plant communities and, therefore, the overall effectiveness of these solutions. The work will include recommendations for restoration measures for ageing and defective infrastructures and for avoiding such problems in new solutions.

Supervisors: Erik Abner, Prof. Elin Org

The aim of this doctoral research project is to elucidate the genetic background of chronic respiratory diseases (CRDs), with a focus on the interplay between respiratory viral infections and host genetics. By leveraging health and genetic data from the Estonian Biobank and nasopharyngeal swab collections, this study seeks to identify genetic risk factors for viral infections and their role in disease pathogenesis. Preliminary analyses have identified promising genetic variants involved in the regulation of lung inflammatory responses, which may mediate the development of RSV bronchiolitis and chronic lung diseases. This project employs bioinformatic methodologies to map the genetic mechanisms of CRDs and their interactions with viral infections. The analysis focuses on the interplay between genetic predisposition and environmental influences, particularly viral infections, in disease development. Additionally, it aims to identify genetic and phenotypic overlaps among chronic lung diseases to distinguish underlying molecular mechanisms and define both shared and disease-specific pathogenic factors. To understand the interplay between host genetics and viral infections, the project will integrate phenotypic and genetic data from viral infection datasets, analyzing correlations between genetic predispositions and infection patterns. RNA sequencing will also be carried out to identify viral strains from nasal swab samples, enabling detailed analyses of how specific viral strains interact with host genetics. These comprehensive approaches will provide insights into the genetic and environmental factors contributing to the development and progression of CRDs in the context of viral infections. The findings from this research will advance our understanding of the links between infectious diseases and chronic conditions, paving the way for personalized prevention strategies and targeted therapeutic interventions.

Supervisors: Jaanika Kronberg, Oliver Aasmets

This aim to explore the role of exposome, metabolites and genetic risk for the development of hypertension. This PhD project will utilise the datasets, knowledge and collaborations from ongoing exposome projects in the Institute of Genomics, by focusing on hypertension which is a risk factor for many non-communicable diseases. The project will also build on ongoing work in the PRG1291 in relation to associations between metabolites and diseases. The project is structured into 3 articles. Article 1 focusses on applying exposome analysis for the development of hypertension. The plan is to use exposome data from the EXPANSE project, phenotype definitions from PRG1291 and hypertension diagnosis data of 210,000 Estonian Biobank participants for survival models. Article 2 combines genetic risk and exposome data. The student will cluster exposome data: air and built environment will be used, with the possibility of social exposome factors from collaborators in the EstBB. Genetic risk scores will be developed based on publicly available summary statistics from the GWAS catalog. The single and combined effects of both exposome clusters and genetic risk will be analysed. Article 3 will build on the results of articles 1 and 2 and analyse hypertension with mediation analysis methods, including both genetic risk and exposome as exposures and metabolites as potential mediators.

Supervisors: Mait Metspalu, Reedik Mägi, Luca Pagani, Vasili Pankratov

Genome sequencing uncovers rare monogenic diseases affecting 5-7% of people. Polygenic Risk Scores (PRS) amalgamate common genetic variants from GenomeWide Association Studies (GWAS) to assess relative genetic risk for complex diseases. PRS enhances population stratification in screening programs, aids health decisions, identifies comorbidities, and groups individuals by biological pathways. Challenges include PRS transferability across diverse populations, uncertainty in estimation, integrating polygenic and monogenic risk, and adjusting for demographic factors. Ancestry-specific PRS accuracy varies within homogeneous groups, prompting research on enhancing ancestry-informed PRS and assessing prediction accuracy in specific cohorts like the Estonian Biobank. Goals of a PhD project include refining ancestry-specific PRS, assessing prediction accuracy in diverse cohorts, comparing LD-scores, and exploring methods to mitigate PRS transferability issues.

Supervisors: Teele Palumaa, Priit Palta

This project investigates the genetic and metabolic mechanisms underlying eye diseases and their associations with lifestyle and systemic conditions using an integrative approach combining epidemiology, genetics, and metabolomics. The doctoral candidate will complete a placement at East Tallinn Central Hospital Eye Clinic to gain insights into the Estonian healthcare system, enabling the development of precise phenotype definitions to facilitate advanced analyses. First, the project will characterise the genetic and metabolic underpinnings of all major eye diseases using rigorous phenotyping from the available healthcare data. Genome-wide association studies (GWAS) and metabolomic analyses will identify genetic variants and metabolite profiles associated with disease prevalence and progression, with findings validated in external datasets like the UK Biobank and FinnGen. Second, the project will examine how lifestyle factors, such as physical activity, nutrition, smoking, sleep, and circadian rhythms, contribute to eye disease risks. Using phenome-wide association studies, GWAS, and polygenic risk scores, the study will uncover shared biological pathways linking lifestyle traits to eye diseases, providing insights for prevention and public health strategies. Finally, the project explores bidirectional relationships between eye diseases and systemic conditions, including cardiovascular disease, diabetes, and hypertension. Advanced downstream analyses such as genetic linkage, pleiotropy, and network analysis will identify shared mechanisms and causal pathways connecting systemic and ocular health. This project equips the doctoral student with advanced research skills and deep expertise in Estonian Biobank healthcare data, enabling impactful contributions to academia and precision medicine.

Supervisors: Lehti Saag, Kristiina Tambets, Alena Kushniarevich

This project focuses on advancing our understanding of the genetic history of the Eastern Baltic through the analysis of ancient DNA (aDNA) and integrating cutting-edge bioinformatic methods. aDNA offers rich insights into past human migrations, adaptation, and social structures, but it poses challenges due to DNA degradation and contamination. Traditional aDNA studies primarily rely on allele frequency differences, but recent developments in imputation methods and haplotypebased analyses provide new opportunities for more precise genetic reconstructions of past populations. The project's aim is to enhance the understanding of the genetic history of the Eastern Baltic using both traditional allele frequency analyses and more sophisticated haplotype-based approaches. The first objective involves processing genomic data from ancient Eastern Baltic individuals and analysing it using allele frequency methods. This will provide a broad overview of genetic affinities and population history. The second objective focuses on haplotype-based analyses, which will allow for finer insights into admixture processes and kinship practices in the region by imputing genotypes with an enriched reference panel. Lastly, the project seeks to explore the relationship between human populations and their environment by gathering spatiotemporal data on material culture, climate, and vegetation. Using this data, the project will model how changes in genetic ancestry intersect with environmental and cultural shifts over time. Overall, the project aims to contribute to a deeper, more detailed understanding of the genetic history of the Eastern Baltic and its broader implications for human population dynamics and past societies.

Supervisors: Triin Laisk, Reedik Mägi

Cyclical fluctuations in female sex hormones, such as estrogen and progesterone, influence women’s physical, emotional, and cognitive health. Sensitivity to these hormones is widespread but often underestimated and underreported. Individual hormone sensitivity is reflected in conditions such as premenstrual syndrome (PMS), responses to hormonal medications, certain pregnancy-related conditions, and (peri)menopausal symptoms. While conditions reflecting hormonal sensitivity are partially heritable, their genetic determinants remain largely uncharacterized. This project is based on data from the Estonian Biobank, which includes 210,000 participants, 136,000 of whom are women. The aim of the study is to map the genetic architecture and biological basis of hormonal sensitivity.

Supervisors: Ene Reimann, Reedik Mägi

In this project, we aim to map the role of genetics and genomics in the development and progression of chronic inflammatory diseases such as osteoarthritis (OA), rheumatoid arthritis (RA), and spondylarthritis (SpA). Rheumatic diseases are very common affecting more than 40% of the European population and cause significant morbidity, pain and shortening of life expectancy. For that, we will study the events leading to disease onset using data from the Estonian Biobank. We will use a standardized GWAS analysis workflow to assess the associations between genetic variations and rheumatic diseases and inflammation in order to identify potential genetic biomarkers. Similarly, we will analyse the associations with metabolite profiles using NMR metabolites available in the Estonian Biobank. These data, together with other available omics datasets and risk scores, will be used to build new risk prediction models for rheumatic diseases. Within this project we also aim to map the shared genetics of different musculoskeletal diseases.

Supervisors: Raivo Aunap, Alexander Kmoch, Meelis Kull

Accurate and reliable large-scale spatial maps of environmental variables, such as greenhouse gas fluxes, soil properties, and biodiversity, are essential for effective environmental management and decision-making. Machine learning techniques have emerged as powerful tools for predictive modelling, offering flexibility and the ability to capture complex relationships within environmental data. However, a critical limitation of many machine learning models is their inherent uncertainty. This project focuses on addressing this limitation by: 1) systematically estimating epistemic uncertainty, which arises from the model's limitations due to insufficient training data or limited covariates; and 2) developing and evaluating innovative cartographic methods for effectively communicating this uncertainty to a diverse range of stakeholders. By explicitly acknowledging and quantifying model uncertainty, this research aims to improve the reliability and trustworthiness of spatial predictions, enhance the confidence of decision-makers.

Supervisors: Mikk Espenberg, Ülo Mander

The carbon (C) and nitrogen (N) cycles involve many complex processes, but the specific microbes responsible for GHG emissions under different conditions remain largely unknown. Forests add another layer of complexity to these cycles due to tree cover, yet the role of the phyllosphere and canopy soil in microbial and GHG dynamics is still poorly understood. Since forests play a crucial role in climate regulation, understanding their sources and sinks of GHG is essential for predicting future climate trends. Human activities and climate change continue to alter C and N cycle processes, making it even more urgent to study their effects on forest ecosystems. While some microbial contributors to GHG fluxes have been identified in some ecosystems, research on their role in forests remains limited. Gaining a deeper understanding of these microbiome-driven processes is critical for predicting forests' responses to environmental changes and their influence on global climate warming.

This PhD project aims to (1) develop methods and a bioinformatics pipeline for studying the microbiome in plant and plant-related materials, and (2) predict the forest canopy’s effect on the microbiome, along with concurrent GHG fluxes.

Supervisors: Siiri Silm, Keiu Telve

The aim of the research project is to advance the understanding of everyday mobility practices, and co-design and carry out interventions in social groups to nudge people toward sustainable mobility behaviours. This research project explores how social, cultural, and structural factors shape mobility behaviours and how co-created interventions with social groups can foster sustainable mobility. While past interventions have primarily targeted individuals, this study explores the potential for groupbased approaches to create long-lasting change.

Drawing on social practice and community theories, it highlights how mobility choices are influenced not only by infrastructure and climate conditions but also by social relationships within families, organisations, and communities.

Using qualitative and mixed-methods research, including interviews, participant observation, and smartphone tracking, the study will analyse everyday mobility practices and design interventions for sustainable mobility. Interventions will be co-designed with institutions and residential communities in Tartu, testing how organisational cultures and social bonds influence and shape behaviour.

Research provides deep insights into the lived experiences and meanings associated with mobility choices, it will give a better understanding on how individual practices and decisions intertwine with group influences in actual mobility practices. By integrating ethnographic insights with co-creative and intervention-based approaches, this project advances sustainable mobility research and test whether group-level interventions can be more effective than individual-focused strategies.

Supervisors: Kadri Leetmaa, Pille Metspalu

This PhD project aims to develop a validated monitoring methodology for assessing the change in environmental qualities in the implementation process of comprehensive spatial plans (national and regional level spatial plan, comprehensive plans of local governments). The project addresses existing research gaps in spatial planning literature, focusing on the effects of various measurements methodologies for assessing environmental quality, on the need to observe environmental qualities in the activity spaces and over the life course of people, and on the understanding of the functions of monitoring methodologies (to be applied in the spatial planning context where the places are in continues change). The monitoring methodology elaborated will be tailored for various settlement types and validated against the most used well-being and mental health metrics. The PhD project is designed according to the knowledge transfer principles, requiring the readiness to collaborate and communicate with the agents on the field (the Estonian community of spatial planners and more widely with the international network of spatial planners). The PhD project also requires the already acquired proficiency to work with different types of spatial data and an experimental attitude in the methodology development process.

Supervisors: Evelin Pihlap, Ivika Ostonen-Märtin, Ain Kull

The European Union has prioritised implementing climate mitigation policies, which has drawn a lot of attention to carbon dioxide removal (CDR) technologies. Forests and grasslands act as a carbon sink, they sequester atmospheric carbon and accumulate it in biomass and soils. This has set a primary focus on implementing afforestation, reforestation and grassland restoration as a nature-based CDR solution. In Estonia, however, this means that huge pressure will be set on agricultural lands and natural ecosystems when altering land use. Such alterations in land use mean that both, ecosystem and soil biogeochemical reactions will be influenced, which ultimately has impact on carbon accumulation, but we do not know the extent of it. Before implementing CDR solutions and conducting any harm to existing ecosystems, it is important to understand the impact of land use shifts to soil properties and evaluate the actual potential in carbon storage. The objective of the doctoral thesis is to investigate influences of land use change in Estonia and determine potentiality and drawbacks on implementing CDR strategies in Estonia.

Supervisors: Kaido Soosaar, Ülo Mander

Wetlands cover 5-8% of the world's land area and have a tremendous capacity to sequester carbon (C) from the atmosphere. Natural wetlands effectively accumulate C effectively due to water-logged conditions promoting highly stable C content. Currently, there is still a great deal of uncertainty regarding the spatial extent of restored wetlands and the extent of C sinks, as well as source estimates and sustainable restoration alternatives. In addition, there are uncertainties related to the impacts of climate change on greenhouse gas fluxes, particularly for extreme weather events such as droughts and floods. Currently, there is a lack of national emission factors to account for GHG fluxes from restored wetlands, especially as it relates to CH4 fluxes. These issues hinder the efficient use of wetlands for GHG mitigation and adaptation in the context of other LULUCF mitigation options.

The dissertation will add to the current state of knowledge on wetlands, their use and degradation in Europe. Several new experimental data on ecosystem responses to wetland management and restoration under different land uses will be collected and summarised in relation to biodiversity and other ecosystem services.

This work will be based on the data obtained from the ALFAwetlands project joint database (27 sites in Europe) and RestPeat (6 sites): soil CO2, CH4 and N2O fluxes, automated and periodic environmental parameters, including precipitation, soil temperature, groundwater table and topsoil moisture, data collected over a two-year period. To establish the soil carbon balance, C transfer in litter, C stocks in above- and below-ground biomass, and C turnover in litter decomposition are quantified by synthesizing high-quality research data and data from field studies. GHG flux data from the database will be linked to detailed information on peat composition, soil and water biogeochemistry to improve process-based modelling of peatland GHG emissions.

Supervisor: Argo Jõeleht

A wide exploitation of geothermal energy is a necessity to achieve renewable energy-related goals set by Estonia and EU. Estonia is rather successful in utilizing near-surface and shallow (up to 200–500 m deep) geothermal energy for private houses and mid-size buildings. However, large installations (e.g. district heating) require usage of substantially deeper (2–8 km) underground. The main aim of the doctoral project is to estimate temperature at greater depth and based on this to evaluate the potential of deep geothermal energy usage in Estonia. Estimation of temperatures at several kilometres depth is not straight forward as the deepest boreholes are only 800 m deep. Temperature gradient depends on geothermal heat flow density in location, thermal conductivity of rocks, palaeoclimatic disturbances etc. An important parameter, heat flow density will be determined for several new boreholes. At the same time existing data will be assessed using new thermal conductivity data and updated knowledge of palaeoclimatic changes in the region. An essential part of ground surface heat flow is produced upper crustal rocks due to radioactive decay of U, Th and K, and therefore these parameters have reasonable correlation. By measuring radiogenic heat production and petrophysical properties of crystalline basement rock drillcores, one can link these data to magnetic and gravity anomaly maps and thus improve heat flow density estimates in areas without suitable wells for heat flow determinations. Combined heat flow density and heat production data will the basis for maps of temperature at different depths that will be further analysed to determine which areas are more suitable for geothermal developments and at what conditions.

Supervisor: Marko Kohv

Flowing waters are vital for nutrient cycles, biodiversity, and human society, yet climate change and land-use modifications are disrupting these systems by increasing erosion, sediment displacement, and habitat loss. This project analyzes the effects of these stressors on the hydrological regimes and sediment transport of Estonian rivers. By integrating high-resolution in-channel measurements, century-long monitoring data, and advanced catchment- and channel-scale modelling, the study will provide a comprehensive view of water flow dynamics under changing conditions. It will also explore nature-based solutions such as floodplain restoration, improved land management, and enhanced river connectivity to mitigate adverse impacts. Key research sites include the Pudisoo River, where high-precision data and restoration measures are already monitored, and a network of 10 wetland pairs from the PRG1121 project, complemented by century-long national hydrological datasets. Cutting-edge techniques—using aerial and water drones, sonars, and ADCPs—will capture unprecedented temporary and spatial details on in-channel flow dynamics. The project is divided into three phases: Phase 1 (years 1–2) examines historical effects of climate change and land-use modifications and tests novel fieldwork technology; Phase 2 (years 2–3) focuses on sediment transport in catchments and channels; Phase 3 (years 3–4) develops integrated models to generalize findings and predict future scenarios, assessing the impacts of projected changes and potential restoration measures.

Supervisors: Kaarel Lumiste, Peeter Paaver

Over the decades, the Estonian oil shale industry has accumulated a large amount of mining and oil shale processing residues, primarily composed of hydrated oil shale ash sediments of various origins and compositions. The recycling of ash waste has been limited, and the valorization of deposited ash waste has not been thoroughly addressed until now. The principal objective of this project is to identify methods for utilizing residual materials from the deposited oil shale industry as secondary raw materials for the production of high-value binders and building materials. A significant portion of the waste from the oil shale industry can undergo chemical activation, facilitating their valorization through chemical-mechanical activation and extraction processes. The project's focus is on the mechano-chemical reactivation of ash and tailings waste deposited in landfills, enabling the repurposing of the waste as alternative alkali-activated binders. This approach aims to reduce the necessity for mining new minerals and minimize our overall environmental footprint.

Supervisors: Riho Mõtlep, Peeter Somelar

In Ida-Virumaa, hudreds of millions of tons of various wastes from the oil shale industry are concentrated. These wastes are currently underutilized but represent potentially valuable secondary raw materials for various industries. The diverse origin, composition, and development history of these waste deposits complicate their utilization. Systematic studies of the composition and heterogeneity of waste can identify different possibilities of use in the chemical industry, the building materials industry, the fertilizer industry and as aggregates. The general goal of the project is to study the composition of Ida-Virumaa's solid waste through a systematic approach and to evaluate the best opportunities for its valorization. To achieve this, an overview of Ida-Virumaa's solid wastes will be undertaken, followed by studies of the composition and heterogeneity of the waste. Possible existing solutions for valorizing the waste for both the chemical and building materials industries will be explored.

Supervisors: PhD Lauri Aarik, Prof. Kaupo Kukli

The aim of the doctoral thesis is to study gallium-oxide-based heterostructures prepared by atomic layer deposition method. To achieve the goal, the synthesis of gallium oxide layers on the surfaces of various oxides (such as tin, titanium, aluminum, chromium and copper oxide) and their mixtures, as well as the growth of those oxides on the surface of gallium oxide, will be studied. The resulting materials and heterostructures will be analyzed and deposition processes will be optimized. The expected results will increase the application potential of novel materials and material combinations in high-tech products. During the 4-year project, the PhD student will have access to state-of-art laboratories and equipment used for material studies. The student will exploit the atomic layer deposition method for the synthesis of abovementioned materials layers and different characterization tools, while studying the elemental composition, structure, optical and electrical properties of thin films. The PhD studies and the thesis is expected to markedly contribute to the development of technologies and methodologies for the preparation and characterization of materials suitable for a new generation of electronic and optical devices. Concurrently, the PhD studies will result in the training and graduation with the PhD degree of a highly qualified and skilled young researcher, who will be able to contribute to the further development of science and technology.

Supervisor(s): Alvo Aabloo, Longfei Chang

Inspired by biological flexibility, soft robotics holds great promise for applications in healthcare, industry, and environmental monitoring. However, accurately modelling the behaviour of these robots is challenging due to the complex interactions between materials, forces, and external conditions. This PhD research focuses on developing a cutting-edge simulation toolbox that combines two powerful approaches: finite element modelling (FEM) for large-scale mechanical behaviour and molecular dynamics (MD) for microscopic interactions, enhanced by AI-driven force fields. The study will also investigate how bacterial growth affects soft robotic materials, a crucial factor for biointegrated robots.

Using COMSOL and MD, the research will create realistic simulations that account for thermal effects, fluid pressure, elasticity, and diffusion. The models will be experimental, ensuring their accuracy in real-world conditions. The outcomes of this work will advance soft robotic design, biohybrid robotics, and medical applications, paving the way for more durable, adaptable, and intelligent robotic systems.

Supervisor(s): Andreas Kyritsakis, Veronika Zadin, Ehsan Moradpur-Tari

High electric fields influence the electronic structure of atoms on surfaces and the dynamics between atoms, thereby creating biased diffusion of surface atoms and affecting both electron and ion emission, as well as physical phenomena such as vacuum arcs. The effects caused by the high electric fields are highly beneficial in many applications, while in others, they are uncontrollable and extremely destructive. This doctoral project focuses on the development of novel machine learning-based multiscale and multiphysics computational models to predict surface modifications induced by high electric fields within the framework of hybrid electrodynamicsmolecular dynamics (ED-MD) simulations.

The development of surface dynamics models is based on density functional theory (DFT) calculations. These are highly accurate but extremely time-consuming analyses. Machine learning models will be developed based on the conducted DFT calculations, achieving accuracy comparable to DFT simulations while significantly reducing computational time. These machine learning models enable a detailed study of materialelectric field interactions in large-scale, combined (ED-MD) multiscale simulation frameworks and allow for atomic-scale predictions of interactions between high electric fields and material surfaces.

This doctoral research contributes to breakthroughs in understanding the physics of high electric field and material interactions. The results have a significant impact on nanotechnology and vacuum arc physics, playing a critical role in the design of particle accelerators, the development of next-generation electron microscopes and X-ray sources, as well as advancements in neuromorphic computing and ion sources for quantum computers.

Supervisor: Tarmo Tamm

The project aims to develop polymer networks from renewable polymer sources with reversible cross-linking mechanisms for recyclable fiber-reinforced polymer composites (FRP). Conventional FRPs, increasingly used in demanding industries such as wind energy, suffer from poor recyclability, leading to environmental concerns due to landfilling and inefficient disposal methods. The project aims to address these challenges by designing bio- based thermoset matrices with dynamically bonded polymer networks, enabling controlled degradation and chemical material recovery.

The approach is structured around four key areas. First, renewable thermoset matrices will be synthesized using bio-based precursors and reversible bonding strategies, such as Diels-Alder chemistry or RAFT polymerization. Second, cross-linking reversibility will be optimized to ensure efficient recycling while maintaining structural integrity. Third, mechanical performance and fiber-matrix interactions will be enhanced through compatibilizers and chemical optimization. Finally, degradation mechanisms will be studied using accelerated methods and modeling to predict material lifespan and failure behavior, considering realistic thermal and mechanical stress-profiles, allowing improved designs for the polymer matrix and bonding to reinforcement.

Supervisors: Kerli Orav-Puurand, Kateryna Lipmaa, Helen Hint

Mathematical language is a complex phenomenon possessing its own vocabulary, grammar, and syntax, while also making use of the regular linguistic constructions, especially in the word problems. Many studies are pointing to the correlation between student’s textual understanding and mathematical literacy, while few are researching the processes underlying this relationship. Assessing exclusively mathematical literacy fairly appears to be a challenging task due to the fusion of colloquial and mathematical discourses. Actuality of reviewing the linguistic content of the mathematical tests is difficult to understate. While students perform worse on word problems than on problems presented in numeric format, they also express concerns about the complexity of linguistic structures used in tests in their feedback. Such discrepancy and the feedback suggest investigation of how modifying the linguistic structures in the test items while preserving difficulty level and semantics would affect student test comprehension and performance.

This project aims to discover what exactly in the wording of mathematical test tasks in the Estonian state examination is influencing the task’s understanding and student’s efficacy in solving. Based on mathematical educational methodologies and fine-grained discourse analysis we plan to find out what type of modifications of a non-mathematical vocabulary and which nuances of the exclusively Estonian linguistic structures in the test tasks could help to achieve better mastery and accomplishment in the mathematics tests and how. The impact of the linguistic modification on Estonian speakers with different levels of language proficiency, as well as on the students of different performance levels in mathematics will be tested in practice. Based on theoretical interpretation, we plan to draw conclusions about the influence of specific linguistic factors on the process of solving mathematical tests. A collection of practical test examples aligned with these factors, as well as the development of an analytical test evaluation tool, will be among the expected outcomes.

As Estonian state took direction toward the modernizing and innovation of the upper secondary school’s final examination test system, its automatization and computerization, the shift to e-environmental assessment calls to revising the content of tests itself. Investigating the language of mathematical state exam tests in the context of the Estonian language will deepen the understanding of its challenges, particularly within Estonian education. This will enable a more precise evaluation of students' purely mathematical skills and make mathematical tests more linguistically inclusive. Integrating mathematics education and linguistics is especially crucial as Estonia transitions to Estonian-language education. The results of this project can serve as a foundation for further descriptive and empirical analyses of the difficulty of mathematical tasks and provide a basis for upgrading mathematical textbooks.

Supervisors: Tiina Kraav, Stanislav Nemeržitski, Maria Fahlgren

Mathematics education plays a crucial role in students’ general education, yet challenges persist in learning STEAM subjects due to various factors. Creativity is increasingly recognized as a key cognitive skill that fosters new approaches to problem-solving. Considered a vital 21st-century skill, creative thinking enhances students’ academic success and adaptability. The 2022 PISA study confirmed a strong link between creative thinking and mathematical competence, suggesting that integrating creativity into mathematics education can deepen students’ understanding of mathematics and develop transferable skills.

Research on creative thinking in mathematics education often focuses on gifted students, yet ‘everyday’ creativity can be nurtured in all learners with proper teacher support. Both the school environment and well-designed learning tasks play a significant role in fostering creativity. Studies in higher education highlight how technology enables the development of computer-assessable tasks that promote divergent thinking. However, in Estonia, evidence-based approaches to designing mathematics tasks that cultivate creative thinking remain underexplored.

This research aims to design mathematics tasks that enhance both subject knowledge and creative thinking while assessing their impact on students’ learning attitudes and outcomes. The study’s practical contribution lies in developing scientifically grounded methodologies to improve mathematics education both in Estonia and beyond.

Supervisors: Märt Möls, Maris Alver

Schizophrenia spectrum disorder patients exhibit a higher prevalence of metabolic syndrome compared to the general population, primarily attributable to antipsychotic use and lifestyle factors, while the role of genetics has remained largely unassessed. When evaluating disease progression trajectories, whether for an individual patient or a small subgroup of patients (e.g., those receiving a novel treatment), optimal parameterization of the model becomes crucial. For accurate predictions, it is essential to minimize the number of estimated parameters, while ensuring that the model can estimate a non-linear trajectory describing disease progression, regardless of its shape.

One possible solution is to apply functional data analysis techniques, such as functional principal component analysis, to construct the model. However, the use of functional data analysis methods for biobank data has been somewhat limited, due to challenges such as censoring and left-truncation. Enhancing functional data analysis techniques to better align with the biobank data framework could improve predictive model accuracy and provide a more comprehensive assessment of the molecular and genetic mechanisms underlying the comorbidity of schizophrenia spectrum disorder and metabolic syndrome.

Supervisor: Ella Puman

The strategic plan of the Faculty of Science and Technology states that a priority for the field is to ensure that students have solid basic knowledge in mathematics for future study. This plan includes developing the targeted use of digital tools in teaching and learning.

In recent years, some students in basic math courses have been dropping out mid-semester or failing the course, even when they put in their best effort. The previous studies have identified gaps in foundational knowledge and difficult topics for students. By employing a constructivist approach, the study seeks to provide insights into students’ experiences and to facilitate a better understanding of their learning processes. Mixed research methods are planned based on the research questions, focusing on what students experience and how they interpret their experiences.

The thesis explores how first-year students adapt to university-level calculus by utilizing feedback surveys conducted over several years and employing qualitative analysis to address the research questions. The study will incorporate student feedback and propose necessary changes to the teaching methods of the courses. An inductive categorization approach will be applied to analyze data from open-ended survey questions.

The primary objectives of the research are to understand the challenges students face in mastering calculus, identify the topics they find most difficult, and evaluate the effectiveness of the learning materials provided. Additionally, the study seeks to determine which digital tools are most popular among students and integrate these preferred tools into the teaching process to create a more cohesive and effective learning experience. The research will also clarify which digital tools are most suitable for specific learning outcomes and topics. As a result of the thesis, necessary changes will be proposed in higher mathematics courses, introducing various digital tools, including using AI capabilities.

Supervisor: Riho Teras

The study focuses on identifying the regulatory mechanisms of Pseudomonas putida biofilm formation, which is used as a cell factory in industry, and on developing and adopting a new reporter system. The research focuses on identifying the expression of the biofilm-associated gene of periplasmic protease LapG. The role of exclusion in the expression of key biofilm-associated genes is analyzed using a reporter system developed in the laboratory. In addition, the effect of global transcriptional regulators and two-component systems on lapG expression and the role of LapG and LapA in biofilm vesicle formation and intercellular communication are discussed. The study contributes to a better understanding of biofilm regulation, bacterial communication, and biotechnological applications.

Supervisors: Prof. Maido Remm, Dr. Epp Songisepp

The primary focus of the food industry is on detecting and controlling foodborne pathogens; however, microorganisms associated with food spoilage often pose a greater risk. These microorganisms enter the production environment through raw materials, packaging, personnel, and other vectors, and are capable of forming biofilms. Biofilms can develop on various surfaces during food processing, including production equipment, pipelines, and packaging materials. Their formation is influenced by the microbial community composition, environmental conditions, the food matrix, and cleaning and disinfection procedures. In industrial settings, biofilm-forming microorganisms can adapt to environmental stressors, including biocides, increasing their resistance to conventional hygiene measures.

The aim of this doctoral thesis is to investigate the potential of metagenomic sequencing to characterize the microbial communities and their genetic potential present in dairy processing environments. The study focuses on the identification of genes associated with biofilm formation and the genetic diversity of biofilm-forming microorganisms. Additionally, it will investigate antimicrobial resistance (AMR) genes and the role of mobile genetic elements (MGEs) in their dissemination, providing insights into the dynamics of resistance and its potential impact on food safety and industrial hygiene practices.

Supervisor(s): Hedvig Tamman, Andres Ainelo

Pseudomonas putida is a ubiquitous soil bacterium that is studied as a promising synthetic biology chassis due to its high stress tolerance and versatile metabolic pathways. Even though we know a lot about its biology as a model organism, data is lacking on the interactions between P. putida and the viruses that infect it – bacteriophages. In biotechnological applications, bacteriophages are a menace since an outbreak in a reactor means costly loss of product batch and a thorough cleanup. The model strain of P. putida is inherently relatively phage-resistant thanks to the numerous phage defense systems found in its genome. The identification of these systems is quite recent and therefore most of these genes are still annotated as unknown function in general databases. It is not surprising that during bacterial genome optimizations for increased bioreactor yields, phage defense systems can easily be discarded as nonessential. Therefore, it is necessary to chart the active phage defense systems of P. putida to ensure optimal phage resistance of strains in industrial applications. Thanks to the CEPEST collection of P. putida phages in our institute, we can approach this problem experimentally. During this PhD project, predicted phage defense systems will be verified as active or inactive, using genomic defense system deletions followed by phage resistance measurements. Additionally, a weakened P. putida strain will be developed that lacks all the predicted defense systems. This strain will be used to expand the CEPEST collection with new phage groups that could not be isolated using the defense-competent original strains. The project also aims to study the synergy of different defense systems using both combinations of system deletions and molecular analysis of the defense proteins. Overall, this allows us to gain a thorough understanding of the phage defense network of P. putida.

Supervisors: Osamu Shimmi, Tambet Tõnissoo

The regulation of tissue morphogenesis is a fundamental aspect of animal development. Our recent findings indicate that the dynamics of microtubule-mediated cell protrusions, known as the Interplanar Amida Network (IPAN), are crucial for three-dimensional (3D) tissue morphogenesis in fruit fly (Drosophila) pupal wings. This is achieved through coordinated mitoses between two layers of wing epithelia. To further understand the physiological significance and molecular mechanisms of IPAN dynamics, this project aims to address how the intercellular network IPAN serves as a framework for the exchange of signalling molecules and organelles during the formation of the 3D pupal wing. By employing in vivo live imaging technology and genetic manipulation tools, the project will focus on the two following objectives. First, we aim to investigate how BMP signalling is regulated by the IPAN as a proproliferation factor. We will unveil how BMP signalling is coordinated between two distinct compartments to sustain coordinated growth. Second, we examine the transport of organelles through the IPAN to sustain 3D morphogenesis. We will explore how mitochondrial transport is regulated via the membrane protrusion network and investigate how such transport plays a role in morphogenesis and what molecular mechanisms are involved. The project will be carried out using multidisciplinary approaches including Drosophila genetics, cell biology, molecular biology, developmental biology, bioimaging, and image analysis. These approaches will enhance our understanding of the mechanisms driving 3D tissue morphogenesis and have implications for both physiological and pathological processes during animal development.

Supervisor: Professor Andres Merits

Alphaviruses (Togaviridae) are enveloped positive-strand RNA viruses that cause encephalitis or fever, rash, and arthritis in humans. Chikungunya virus (CHIKV) is the most medically significant (>400,000 cases in 2024) alphavirus but currently no antiviral treatments exist. Alphaviruses form membrane-bound replicase complexes in infected cells, involving viral nonstructural proteins (nsPs), RNAs, and host components. In this project we apply RNA-based approaches to study early infection stages of alphaviruses using trans-amplifying RNAs (taRNAs), which have potential in RNA vaccines, cancer therapy, and gene technology. The study aims to include: (1) real-time tracking of viral RNA dynamics using SunTag markers, (2) analysis of replicase functions and mechanisms of action of antiviral inhibitors, (3) develop and apply method to evolve viral RNAs for optimized replication and resistance, and (4) develop new taRNA-based tools for vaccines and cell therapies. The PhD student will also contribute to ongoing research.

Supervisors: Hannes Kollist, Dmitry Yarmolinsky

Majority of plant gas exchange with the surrounding environment occurs via stomatal pores which are formed by the pair of guard cells. Signaling systems in guard cells adjust the pore size and by that optimize the two basic plant growth parameters; influx of atmospheric CO2 for photosynthesis and loss of water by transpiration. Guard cells can respond to environmental and internal factors, including changes in CO2 concentration, light/darkness, hormonal signals, and air pollutants such as ozone. Reactive oxygen species (ROS) and Ca2+ are involved in the activation of signalling cascades controlling stomatal movements as second messengers. Our previous forward genetic screen led to the identification of stomatal mutants with strong insensitivity to environmental cues and abscisic acid. Further analysis demonstrated the affected functioning of small GTPases in these mutants, which control cytoskeleton organization and ROS formation. This PhD project will study the roles of small GTPases in guard cells during stomatal movements triggered by environmental cues. Furthermore, the interplay between various small GTPases in stomatal regulation will be studied. To address these research questions, the doctoral student will be trained in various methods of genetics, biochemistry, molecular and cell biology. Transgenic lines and mutants will be studied to characterize the impacts of small GTPases on stomatal regulation. ROS, Ca2+, and the cytoskeleton in plant cells will be visualized using fluorescent reporters. The outcome of the PhD project will clarify how guard cells perceive environmental stimuli and control gas exchange between plants and ever-changing environment. The results will be beneficial in breeding programs for crops with optimized water-use characteristics and productivity.

Supervisor(s): Tanel Tenson, Niilo Kaldalu. Kristiina Vind

Antibiotic resistance is one of the biggest health problems in the world today. Controlling its spread requires different approaches depending on the nature of the infection.

In this research project, the PhD candidate focuses on two key areas:

- Rapid intervention for acute infections (e.g., sepsis).

- Antibacterial treatment for chronic, slow-progressing infections.

To address acute infections, the PhD candidate is evaluating a new antibiotic susceptibility testing method for the rapid determination of resistance. This method can assess bacterial susceptibility within two hours, offering a simple and inexpensive alternative to conventional methods that require days. The candidate has already demonstrated that the method’s working principle is applicable. The next step is to assess its suitability for clinical use by testing a range of clinical isolates and antibiotic combinations.

For chronic bacterial infections, the doctoral candidate builds on previous research to evaluate practical applications of identified compounds. A drug repurposing screen previously identified compounds effective against non-replicating Gram-negative bacteria. In this project, these compounds are tested against collections of clinical isolates to determine their potential as drug candidates. Additionally, a drug repurposing screen is being conducted to identify candidates effective against non-multiplying Gram-positive bacteria and mycobacteria.

Supervisors: Dr. Helena Sork, Assoc.Prof. Taavi Lehto

Genome editing has evolved significantly through different technologies of which the innovative CRISPR/Cas system offers unmatched simplicity, precision and efficiency compared to its predecessors. However, the lack of efficient delivery vectors hinders the broader application of such technologies in precision medicine. The presently available delivery systems encounter issues related to safety, immunogenicity, stability, and poor delivery efficacy to many body organs. A promising alternative is to use extracellular vesicles (EVs), natural carriers capable of transporting macromolecules and targeting specific cells. Since producing pharmaceutically acceptable EV-based carriers is not achievable with current knowledge and technologies, this project aims to develop minimal-component EV mimetics as an alternative, preserving key EV characteristics and ensuring efficient delivery of gene editing compounds. In this project we will first identify key lipid components of EVs to understand the fundamental building blocks needed for creating effective EV mimetics. Next, formulation technology for producing EV mimetics with gene editing compounds will be established, optimized and evaluated in cell culture models. The project will also employ molecular engineering strategies to enhance the stability and cell-specific pairing/targeting capability of the EV mimetics. Finally, the delivery efficacy, biodistribution, and gene editing potency of the leading EV mimetic candidates will be evaluated in vivo, using mouse models. Through aforementioned, the project strives to overcome existing challenges in gene therapy delivery and bring significant advancements to the field of precision medicine.

Supervisors: Kaspar Valgepea, Clara Carneiro

Estonia needs to improve recycling of plastic waste, and valorisation of wood and agricultural waste. Combination of gasification and gas fermentation technologies allows to valorise these waste streams using microbial cell factories to produce chemicals and fuels from gasified waste, i.e. syngas. Although both technologies have been deployed at industrial scale, lab-scale testing and optimisation using local waste (plastic, wood, straw) is needed for a preliminary assessment of industrial applicability of the platform in Estonia. This doctoral project aims to contribute to the development of an integrated gasification-gas fermentation platform by performing gas fermentation experiments using various gas mixes, engineering of acetogen cell factories, and optimising the gas fermentation process.

Supervisors: Lauri Vares, Livia Matt, Patric Jannasch

Praegune plastitööstus baseerub fossiilsel toormel ning lineaarsel majandusmudelil, millega kaasneb suur kogus erinevaid jäätmeid, saastet ja kõrged kasvuhoonegaaside emissioonid. Kliima- ning keskkonnaeesmärkide täitmiseks tuleb drastiliselt suurendada materjalide taaskasutust ning järk-järgult vähendada fossiilse toorme kasutamist.

Taaskasutuse mõttes on eriti problemaatilised nn termoreaktiivsed polümeerid, kus polümeeride ahelad on omavahel ristsidestatud. See muudab küll materjalid äärmiselt tugevaks, kuid kuumutades ei pehmene ja seetõttu ei võimalda mehhaanilist ümbertöötlemist. Sellised materjalid on aga kasutuses näiteks liimides, tugevates pinnakatetes, tuulikute labades, jm.

Käesolevas projektis disainitakse ja sünteesitakse biotoormest sellised termoreaktiivsed polümeerid, millel on võimalik ümbertöötlemiseks ristsidestused eemaldada ja seeläbi muuta polümeer mehhaaniliselt ümbertöödeldavaks. Pärast ümbertöötlemist on võimalik polümeeriahelad uuesti ristsidestada, muutes materjali taas tugevaks. Polümeerid baseeruvad peamiselt ligniinist saadavatel kemikaalidel ning pöörduvaks ristsidestuseks kasutatakse erineva pikkusega süsinikahelaid. Uurimistöö on osa äsja käivitunud "Strateegilise mineraalse- ja süsinikupõhise ressursi ringmajanduse tippkeskusest", 2024-2030.

Supervisors:Kaspar Valgepea, Nilesh Sheshrao Kolhe

Gas fermentation is an attractive technology for recycling carbon oxides (CO and CO2+H2) from waste feedstocks into value-added bioproducts using acetogen microbes. Metabolic engineering of acetogen cell factories, however, is impeded by limited testing capacity of genetic designs due to slow and labourintensive workflows. Recently, cell-free approaches have been successfully used to guide acetogen metabolic engineering by allowing to explore a larger space of genetic designs. This doctoral project aims to test genetic parts and optimise a recombinant pathway using a cell-free system to improve autotrophic production of a target compound in the gas-fermenting model-organism Clostridium autoethanogenum.

Supervisors: Raivo Jaaniso, Valter Kiisk, Madis Einasto

Semiconductor gas sensors are popular due to their high sensitivity and small footprint, but their selectivity and stability have remained relatively low. The doctoral project is focused on developing advanced methods for improving the stability of the gas sensors and broadening the scope of detectable volatile compounds. Involving a joint effort by the Institute of Physics and Evikon MCI OÜ, the study compares and integrates two different approaches: a physical model of aging and machine-learning applied to sensor arrays. The volatile compounds present in human breath are of particular interest. An e-nose platform, targeting personal medical diagnosis of neurodegenerative diseases, will be implemented.

Supervisors: Marco Kirm, Vitali Nagirnõi

Scintillators are materials converting ionizing radiation and particles into low-energy radiation appropriate for photodetectors. The aim of our project is to create a path for new generation of ultrafast scintillators with time resolution in ps time domain. A concept of electronic band structure engineering will be applied to ternary compounds with complicated valence band structure, which creates favourable conditions for the appearance of intrinsic emissions, such as cross-luminescence and intra band luminescence, with short sub-nanosecond decay times. Suitable materials will be selected on the basis of open databases (e.g., AFLOW), containing the results of theoretical studies of material properties and the experimental results available. Thereafter the selected materials will be synthesized as pure compounds and their solid solutions, their properties modelled and studied at the home laboratory and at large scale facilities (MAX IV Lab, DESY Photon Science) using time resolved luminescence spectroscopy with sub-nanosecond time resolution at its world top level. These materials will be beneficial for very different areas of technology, including high-energy physics (CERN), homeland security, controlled nuclear fusion research and especially medical diagnostics. Significantly improved time resolution will cause a breakthrough in positron emission tomography by allowing nonexpensive low-dose scans for pediatric, prenatal and neonatal diagnostics in every major hospital, allowing early identification of tumours and other harmful conditions.

Supervisors: Piia Post, Hannes Keernik

In recent decades, the Baltic Sea region has warmed faster than the global average, a trend expected to continue throughout the 21st century. However, climate projections for the region vary widely and remain uncertain. While all models project warming, the magnitude and associated changes in other climate parameters differ significantly. This project aims to identify the sources of these discrepancies by analysing how regional climate change in the Baltic Sea region depends on large-scale atmospheric dynamics. A better understanding of these uncertainties will improve the physical interpretation of climate change in the region. The findings will also support the planning of adaptation measures for both low-wind periods and extreme storms.

Supervisors: Margus Saal, Manuel Hohmann

Despite being in excellent agreement with different observations, general relativity is facing several challenges. The cosmological standard model based on general relativity is facing the “Hubble tension”, an apparent contradiction between different measurements of the Hubble parameter. General relativity is further challenged by its incompatibility with quantum theory and gauge theories in particle physics. Teleparallel gravity theories are an important candidate to address these open questions due to their modified cosmological dynamics and relation with gauge theory. However, they are also contested by the strong coupling problem, which denotes difficulties with the perturbative expansion of the field equations around highly symmetric background solutions, such as the homogeneous and isotropic cosmological background. This project tackles these issues by studying spacetimes with reduced symmetry, their theoretical consistency and connection to observations, and possibly obtaining observational constraints on the parameters of specific models by applying a Markov chain Monte Carlo analysis. Conventionally, cosmology is based on the assumption of homogeneous and isotropic (and thus highly symmetric) spacetime. Dropping this assumption, one may study cases of lower symmetry, such as the homogeneous Bianchi spacetimes, three of which retain one rotation symmetry. Such models are motivated by an anomaly observed in the cosmic microwave background, denoted the “axis of evil”. We study both the background dynamics of teleparallel Bianchi spacetimes, aiming for solutions which are originally anisotropic, but become isotropic at late times, and their perturbations, which describe gravitational waves and matter density perturbations. We focus on the newer general relativity and scalar-torsion types of teleparallel gravity theories. The former is a simple class which allows us to develop the methods we will be using, while the latter is a large class of theories from which phenomenological models can be built. These models can then be compared to previous work on scalar-tensor theories, allowing us to apply previous results to these new models.

Supervisors: Laur Järv, Tomi Sebastian Koivisto

General relativity which encodes both gravity and inertia within the geometric description of spacetime has been highly successful experimentally. Meanwhile, since inception, the lack of a satisfactory definition of gravitational energy has been a nagging problem on the theoretical side. Later investigations in the context of black holes have shown, that at best it is possible to postulate global notions of gravitational energy, momentum, and angular momentum as integrals over the whole spacetime, and combine those quantities into four statements that mirror the laws of thermodynamics. A breakthrough came only very recently under the name of “general parallel relativity”. It turns out that within a teleparallel extension of the underlying geometric framework of the theory, such conceptual distinction between gravity and inertia is possible which allows a consistent local definition of gravitational energy, while the field equations and classical dynamics remain unaltered from general relativity. The aim of the PhD project is to test these ideas in the case of nontrivial rotating (Kerr-Newman) solutions: first to derive the local gravitational energy of the solution, then to define in general and derive for the particular solution the local momentum and local angular momentum, and finally to formulate the comprehensive local laws of spacetime thermodynamics and explicitly check those in the given case. The current PhD project contributes to the objectives of the team grant PRG2608.

Supervisors: Tomi Sebastian Koivisto, Manuel Hohmann,Ville Vaskonen

Gravitational waves constitute an important part of the current gravity phenomenology. Recent and ongoing observations study the polarization and propagation speed of gravitational waves, as well as the dynamics of their sources. This data has become an excellent and still improving tool for testing the viability of gravity theories, which is an important task in modern theoretical physics.

This project aims at studying gravitational waves in the new, consistent gravity theories motivated by both the long-standing theoretical problems of general relativity and by the current observational issues in the standard model of cosmology. The new paradigm describes gravity on the same footing with the Yang-Mills gauge theories of particle physics interactions, and predicts the emergence of the metric spacetime with a novel chiral structure. Extensions of the gravitational theory in this framework further predict modifications of the metric dynamics which could potentially be attributed to some of the unexplained cosmological observations. The predictions can be confronted with the current and future precision data on gravitational waves, in particular by studying the different polarisations of the waves and their propagation speeds. A variety of aspects of gravitational waves in the new gravity theories will be investigated, potentially including their generation in phase transitions in the very early universe.

The project contributes to the objectives of the team grant PRG2608. The work is carried out within the WG4 (Gravitational theory) of the center of excellence TK202, aiming at results which form a basis for future collaborations with WG1 (Cosmology and astrophysics) and WG2 (Gravitational wave phenomenology).

Supervisors: Tapashi Binte Mahmud Chowdhury, Peeter Burk, Pedro Reis

Socio-scientific issues (SSIs) are complex, real-world problems with scientific and societal dimensions. This study examines how socio-cultural contexts influence teacher perceptions of challenges and strategies for integrating SSIs in science education across Estonia, Portugal, Finland, and Ireland. It aims to develop a conceptual model of socio-scientific literacy, explore the role of science conceptualization in SSI teaching, and compare cross-cultural perspectives. Using qualitative methods, including interviews with science teachers, the study seeks to inform curriculum development, teacher training, and policy recommendations for enhancing SSI-based instruction and fostering informed, responsible citizenship.

Speciality admission requirements in Science Education :

• the Doctoral project
• admission interview

In the first stage of assessment, the doctoral thesis project will be assessed. In the second stage of assessment, there is an interview.

Both the doctoral thesis project and admission interview are assessed on a scale of 0 to 50 points, the minimum positive score is 35 points. Each components gives 50% of your overall score with a maximum overall score of 100 points. If you score at least 35 points out of 50 for the thesis proposal, you will be invited to attend an entrance interview.

Requirements for the content and form of the doctoral thesis. Here you will also find who to contact with questions.

The lenght of the doctoral thesis project is up to five A4 pages (without bibliography).

The project presents

· the research challenge,

· explains its innovativeness and importance, based on the scientific literature,

· the research methodology proposed and the resources needed to address it,

· describes how to expected results are relevant in practice and on the international scientific community.

The candidate is provided with information on the basis of which the candidate can get an overview of the candidate's research potential:

· information on previous scientific activities (incl. working in positions containing research tasks),

· a list of published scientific publications and presentations at conferences.

Doctoral thesis project assessment criteria (at least 35 points are required to fulfill the acceptance condition):

1. innovativeness and importance of the doctoral thesis (up to 10 p);

2. justification of the research problem (up to 10 p);

3. adequacy of research methodology (up to 10 p);

4. feasibility of the design (up to 5 p);

5. candidate's research potential for a design-based doctoral thesis (up to 15 credits).

During the admission interview, the candidate will interpret his/her doctoral thesis in English for 5 minutes, followed by an interview with the committee for up to 25 minutes.

Criteria for the evaluation the interview (at least 35 points are required to meet the admission requirement):

1. ability to dechipher the plan of the doctoral thesis (incl. research problem, related scientific literature, methodology) (up to 15 p);

2. motivation to study for a doctorate in education and to work in the field (up to 5 credits);

3. wider analytical and generalization skills in pedagogical topics (up to 10 p);

4. orientation in the problems of Estonian and world educational life (up to 10 p);

5. self-expression skills (all candidates have the ability to express themselves in English, candidates who speak Estonian as their mother tongue also have the ability to express themselves in Estonian) (up to 10 p).

Speciality representative: Miia Rannikmäe, miia.rannikmae@ut.ee

Supervisors: Elmo Tempel, Radu S. Stoica, Rien van de Weijgaert

This project aims to detect and characterize the walls and voids in the spatial galaxy distribution using
stochastic geometry and persistent topology. The first goal is to create a stochastic geometric model
for the pattern of randomly distributed flattened walls, forming the cosmic web. This model will trace
large walls in galaxy redshift surveys and characterize their properties, such as size, surface density,
and connectivity.
The second goal is to extend this framework to detect cosmic voids, which are crucial elements of the
large-scale matter distribution. These voids have evolved from primordial matter troughs and are
sensitive to dark energy, dark matter, and gravity. The project aims to define voids accurately using a
Bayesian stochastic geometric formalism, allowing precise statistical characterization of their volume,
shape, and other aspects.
The project will use Gibbs marked point processes to model walls and voids in the spatial point
distribution, suitable for pattern detection in galaxy surveys. Additionally, it will investigate the
connectivity of filaments, walls, and voids in the cosmic web using algebraic and computational
topology. By analyzing persistence diagrams and Betti numbers, the project will infer the hierarchical
topology of the cosmic web.
Combining geometric and topological aspects with statistical characterization, the project aims to
develop methods to infer cosmological constraints from the spatial galaxy distribution. This
framework will be applied to real galaxy redshift surveys like DESI, 4MOST, and Euclid, aligning
with ongoing and upcoming observational surveys.

Supervisors: Krista Alikas, Riho Vendt, Viktor Vabson

Water leaving radiance is a key parameter for ocean colour (OC) satellite radiometry. It is the basis for higher order products (e.g. chlorophyll a) and subsequent spatiotemporal analyses. Measurement schemes for in situ above-water radiometry are already well addressed, but in-water measurements, despite considered more accurate, still need attention. Optical laboratory facilities at Tartu observatory will be advanced to allow the characterization and calibration of in-water radiometers, assuring the traceability of a measurement and uncertainty budget derivation when moving from controlled laboratory to variable outdoor conditions. Outdoor comparisons of common radiometers in various deployment strategies, together with the development of new sensor prototype will help the community to optimize the inwater measurements. This allows producing traceable in situ measurements required for every OC satellite mission for validation, vicarious calibration and algorithm development.

Supervisor: Tiina Liimets

Symbiotic binaries represent a crucial yet shortly lived late stage in the evolution of certain low-mass stars. These systems contain a hot component accreting matter from a red giant donor, potentially leading to Type Ia supernovae and enriching the universe with essential heavy elements. Intermittent outbursts from the accretion disk, along with interacting stellar winds, produce complex, extended nebular structures characterized by diverse shapes and high-velocity jets. These nebulae provide a unique opportunity to study the history of mass loss, the forces driving various outburst events, and the mechanisms shaping their morphology—key aspects of symbiotic activity that remain poorly understood. This PhD project aims to investigate several of these nebulae in detail to address these questions. It will involve a comprehensive imaging survey of extended emission around known symbiotic binaries to better understand their formation mechanisms. Additionally, it will include in-depth studies of selected symbiotic nebulae, for example R Aquarii, CH Cyg, HM Sge, V1016 Cyg. For the project, local Estonian telescopes, as well as telescopes in other astronomical sites will be used.

Supervisors: Lea Hallik, Erko Jakobson, Margit Aun

Healthy wetlands provide numerous ecosystem services such as water purification, flood
control, and carbon sequestration. Wetlands play a critical role in mitigating climate change
impacts like floods and droughts. Understanding how restored wetlands respond to climate
change can inform adaptation strategies for both wetlands and surrounding communities.
Earth Observation (EO) data from satellites can cover vast areas quickly and repeatedly,
offering a cost-effective way to monitor restoration progress across entire landscapes in a
standardized way facilitating comparisons and knowledge sharing. The project will contribute
to the development of standardized EO-based protocols for wetland monitoring, which can be
easily adopted by other restoration projects and agencies.
COPERNICUS Services provide unprecedented amount of temporally and spatially
continuous data. Climate predictions are provided at various timescales from seasonal
forecasts to long-term climate projections for different scenarios. Re-analysed databases such
as ERA5 provide spatially continuous time-series of historic climate data. Earth Observation
satellites provide long time-series of monitoring data and derived products (e.g. land cover
classes, biophysical products, phenology metrics). Combining EO data with past climate
information can help to identify key drivers influencing restoration success, like precipitation
patterns, temperature changes, or human activities. Integrating climate projections with EO
data can predict potential challenges for the restored ecosystem, allowing for proactive
adaptation strategies. The combined analysis of EO and climate data can deepen our
understanding of how wetlands respond to restoration efforts and climate change, providing
valuable insights for future strategies of climate change adaptation.

Supervisors: Elmo Tempel, Pekka Heinämäki

This project studies Brightest Cluster Galaxies (BCGs) to understand cosmic structure formation.
BCGs are extremely bright, allowing observations at high redshifts. The 4MOST survey, starting in
2026, will observe BCGs and their environments across the southern hemisphere, providing new
insights into their formation within the cosmic web.
BCGs are typically found at the centres of massive, relaxed galaxy clusters. The 4MOST survey aims
to understand the connection between clusters and the filaments that feed them, which is crucial for
BCG evolution.
Galaxy clusters and groups form a continuum with no clear boundary between them. Understanding
BCGs across this mass range is important. Observational dataset, combined with simulations, will
help study BCGs and brightest group galaxies (BGG), their host systems, and their connection to the
cosmic web.
The 4MOST survey will extend the analysis to less massive groups. The upcoming 4MOST WAVES
survey will significantly increase the number of observed groups and their member galaxies.
Hydrodynamical simulations will provide detailed insights into BCG/BGGs and their co-evolution
with host systems.
The main goal of this PhD research is to understand how BCG/BGGs and their host systems form and
evolve in galaxy groups of different masses, and how these processes differ from those in galaxy
clusters.

Supervisors: Shishir Sankhyayan, Elmo Tempel

This PhD project aims to understand the dynamics of the Universe’s largest structures - superclusters
and voids - and their impact on large-scale matter flow. Superclusters are the largest over-densities
present in the Universe, while voids are the enormous under-dense regions, almost empty at their
centres. By studying these vast structures and their spatial correlation, we aim to get deeper insights
into the fundamental constituents that shape the Universe, including dark matter and dark energy.
Superclusters are not gravitationally bound systems, yet their gravitational potential affects how
matter moves within them. Similarly, voids continuously expand, pushing matter outward and shaping
the cosmic web. We will use advanced simulations and astronomical data to analyse these motions,
developing physically and dynamically motivated new ways to define and measure voids and
superclusters more accurately.
A key goal is to explore whether the distances between superclusters and voids remain stable over
time, making them potential “standard rulers” for testing cosmological models. If successful, this
approach could provide a new method for measuring the Universe’s expansion and refining our
understanding of dark matter and dark energy.
By combining state-of-the-art computational techniques with the latest observational data, this project
will help understand the role of superclusters and voids in forming large-scale structures, offering a
fresh perspective on the largest building blocks of our universe.

Supervisors: Rain Kipper, Indrek Vurm

A significant part of our knowledge about the
structure of the Universe and the processes that control it relies on large-scale
cosmological simulations and their validation through observations. One of the
critical building blocks in these simulations is stellar feedback, i.e., the energy and
momentum deposited into the interstellar medium by massive stars and supernovae,
which has a significant impact on the overall evolution of galaxies. The details of
feedback remains unresolved in many aspects. This doctoral thesis focuses on two of
these aspects: the detailed description of the interaction between the material ejected
by supernovae and the surrounding environment, and the consideration of the
diversity of stellar explosions in feedback calculations. In this context, recent years
have seen significant developments with the discovery of several previously unknown
types of supernovae, including so-called superluminous supernovae, whose energy
budget is 1-2 orders of magnitude greater than that of ordinary supernovae.
Methodologically, we approach the problem on three different complementary levels:
analytically, numerically (using simulations), and observationally, with the aim of
achieving the most comprehensive and observation-validated quantitative picture
possible.

Supervisors: Jan Pisek, Evelyn Uuemaa

The rapid erosion of biodiversity poses a significant environmental challenge. Assessing
biodiversity through ecological field data encounters various challenges, particularly in
gathering reliable information for large areas. There is a pressing need for operational
techniques utilizing remotely sensed data to aid ecologists and decision-makers. This thesis
aims to explore diversity indicators derived from optical imagery, based on the spectral
variation hypothesis. According to this hypothesis, the diversity of spectral patterns across
spatial grids reflects greater niche heterogeneity, facilitating coexistence among organisms.
First, this thesis will identify, qualify, and compare methods for operational biodiversity
monitoring using remote sensing data. The concept of spectral species has recently emerged,
suggesting that spectral heterogeneity at a landscape level corresponds to distinct subspaces
with similar spectral signatures. Using Estonia as a case study and available remote sensing
data, this thesis work will investigate the identification of these subspaces as individual
spectral entities - "spectral species." Finally, remote sensing data and identified associated
drivers will be integrated and processed within a data cube and machine learning models to
allow spatially predictive biodiversity modelling. The aim of this thesis is to enhance
standards for biodiversity mapping using remotely sensed data and to contribute to the
identification of pertinent Remotely Sensed enabled Essential Biodiversity Variables.