Open calls in doctoral studies

Supervisors: Lauri Saks

Bycatch, the incidental entanglement of non-target species in fishing gear, has been identified as one of the major threats to seabird and aquatic mammal populations worldwide. Baltic Sea is a globally important area for wintering and migrating seabirds where bycatch in gillnets and fyke nets represents an important cause of human-induced mortality for seabird and mammals. Therefore, impacts of by-catch on the status of species and the mitigation measures to reduce such impacts have become an acute field of study during past decade and have been set as priorities in international conservation cooperation (HELCOM, ICES) and legislation (EU Marine Strategy Framework Directive). Still, due to sporadic and aggregated nature of bycatch events and often reluctance to cooperate by fisheries still considerable caps in knowledge of the extent and effect of bycatch to marine mammals and birds.

The aims of current doctoral project are: (1) to describe the extent of accidental bycatch of birds and mammals in passive commercial fishing gear of Estonian coastal fishery; (2) to clarify how is bycatch of waterbirds associated with spatial arrangement of fisheries and birds; (3) to undertake an inspection of scientific da-ta on currently proposed measures to mitigate bycatch of birds, together with an analysis to assess the suitability of such measures to small-scale coastal fisheries in the Baltic Sea; (4) to assess the effect of different mitigation measures to avoid bycatch in gillnets during a field experiment.

Supervisors: Arvo Tullus, Ahto Agan

Supervisor: Marina Semchenko

Agricultural grasslands are important for food security but also can provide many other ecosystem services and support high biodiversity. In the last century, the majority of European grasslands have been transformed as a result of land use change. Grasslands on productive soils have been modified by fertilisation, while grasslands on marginal land are no longer mown or grazed. Remaining traditionally managed grasslands suffer from habitat loss and fragmentation. Both management intensification and abandonment result in declines in species richness and deterioration of ecosystem services. However, recent evidence suggest that land use change can also result in loss of genetic diversity and evolutionary changes within plant populations. How such population-level changes scale up to ecosystem functioning and resilience remains unknown. This project will use a variety of grassland systems across Europe to establish how land use intensification, abandonment and broader landscape change affect plant populations, with particular focus on wider consequences for community-level productivity, soil microbial functions and resilience to extreme drought events as a climate change factor. The project will expand our understanding of selective pressures imposed by land use on grassland populations and has the potential to inform future approaches to sustainable and management and biodiversity conservation.

Supervisor: Vladimir Mikryukov, Mohammad Bahram

In ecology, more and more attention is being paid to community functioning, which in the case of microorganisms can be estimated based on a metagenomic approach. This project aims to enhance current methods and databases for soil metagenome analysis and carry out a pan-European survey using data from the LUCAS project. The main focus will be on the genes responsible for antibiotic- and stress-resistance, and involvement in carbon and nutrient cycling. This project will facilitate future metagenome-based analyses of community functioning and enable the assessment of the climate change impact on the ecosystems in different regions of Europe.

Supervisors: Tsipe Aavik, Mari-Liis Viljur, Virve Sõber

Loss and fragmentation of natural and semi-natural habitats over the last century have jeopardized all components of biodiversity. Furthermore, many important interactions between organism groups, such as plant-pollinator interactions, and related ecosystem functions have been under growing threat due to land use change. These processes may eventually translate into the reduction of ecosystem services vital for human well-being, including the services ensured by pollination. Hence, good knowledge about plant-pollinator interactions in contemporary agricultural landscapes is essential for halting the future loss of vital ecosystem services. The PhD project will explore different aspects of plant-pollinator interactions in agricultural landscapes. First, the study will examine the patterns of major groups of pollinators in response to the amount and connectivity of natural and semi-natural habitats in Estonian agricultural landscapes. Second, using pollen metabarcoding tools, the study will assess the availability of floral resources for pollinators depending on landscape context. Third, the PhD project will evaluate the diversity and structure of plant-pollinator interaction networks in contemporary agricultural landscapes. The knowledge obtained from the thesis, which will be carried out in close collaboration with several scientific projects, is not only needed for advancing the fundamental understanding of factors influencing plant-pollinator interactions, but is also highly relevant for designing effective pollinator-friendly landscape management schemes.

Supervisors: Teivi Laurimäe, Liina Kinkar, Marion Wassermann, Urmas Saarma

Cystic echinococcosis (CE) is a life-threatening zoonotic disease caused by a group of tapeworm species collectively referred to as Echinococcus granulosus sensu lato (Eg-sl). This disease is of major importance to both livestock and human health, with annual costs associated with CE being estimated to be US$ 3 billion for treating cases and losses to the livestock industry. The World Health Organization (WHO) has prioritised the control, elimination, and eradication of CE by 2030, and considers the disease as one of the Neglected tropical diseases (NTDs) – a group of 20 conditions of major health and socioeconomic importance (WHO, 2020). Although previous works have provided valuable first insight into the mitochondrial genetic variation of Eg-sl predominantly using single mitochondrial (mt) genes, the extent of mt and nuclear genomic variation is largely unknown. The current PhD project will focus on the genomic investigation of Eg-sl globally, particularly in epidemiologically complex regions, such as Africa. Several factors contribute to this complexity, such as the presence of multiple Eg-sl species with distinct transmission patterns, host preference, prevalence, and pathogenicity. The aim of the project would be to address the following: (i) the extent and nature of genetic variation of Eg-sl in Africa and other regions of the World; (ii) patterns of intraspecific population structure; (iii) whether patterns of mt genetic diversity in the studied Eg-sl species are reflected in the nuclear genome. Fine-scale genetic structuring of the parasite might ultimately lead to an enhanced understanding of the transmission patterns across its distribution range, and whether intraspecific variants might have distinct transmission ecology, host preference and/or other traits of epidemiological relevance.

Supervisor: Toomas Tammaru

In insects in general and in Lepidoptera in particular, there is substantial variation in phenological patterns, one striking example being the “winter moths” in which adult period occurs late in autumn or early in spring. The characteristic set of traits associated with winter moths, e.g. no adult feeding and polyphagy often accompanied by female flightlessness and eruptive population dynamics, have received considerable research attention under the life history theory as well as due to the frequent pest status of these moths. However, previous attempts to explore associations between different “winter moth” traits have fallen short due to being unable to incorporate phylogenetic information. The proposed doctoral project will use the complete phylogenies for two large Lepidopteran families to explore the evolution of distinct phenological patterns and associated adaptations in insects using winter moths as an example. The project incorporates five research questions. The first study aims to re-evaluate the classical traits associated with winter flight in a phylogenetic framework and to establish evolutionary causality. The second, third and fourth study will focus on traits putatively associated with winter flight but unexplored in this context thus far: antennal morphology, wing shape and wing transparency. The final study will explore the temperature-dependence of spring phenology in winter moths, using phylogenetically informed methods to compare the thermal reaction norms of adult eclosion between spring-flying moths and related taxa with different phenology.

Supervisors: Georg Martin & Christopher Hepburn

Kelp forests have global importance as foundation species, providing critical ecosystem services to marine coastal environments such as habitat provisioning, carbon sequestration and nutrient cycling. Bladderwrack (Fucus vesiculosus) and Giant kelp (Macrocystis pyrifera) are two dominant macroalgal species that form important ecosystems in the Baltic Sea and temperate South Pacific, respectively. These macroalgal species possess carbon concentrating mechanisms (CCMs), where the inorganic carbon source is converted to abundant HCO3- (~91% of carbon in seawater) when CO2 concentrations are limited (~1% of carbon in seawater). Therefore, Dissolved Inorganic Carbon (DIC) should not limit primary productivity, with light and nitrogen often being the limiting inputs. This is supported by experimental observations of M. pyrifera under future ocean acidification conditions, which showed that growth and the photosynthetic rate were not affected by altered DIC availability, likely due to CCMs preventing carbon limitation. Literature data suggests that during optimum conditions, DIC can decline below saturation for photosynthesis in dense giant kelp forests in the South Pacific Ocean. However, the process of DIC limitation in dense M. pyrifera and F. vesiculosus forests is poorly known. During optimum conditions (e.g. periods of high light, sufficient water movement and pH) when thresholds for C limitation are applied to reports of DIC concentrations in kelp forests, there is an indication that C availability likely provides the ceiling to primary production in these near coastal ecosystems. Current study will combine temporal and spatial (in 3D) patterns of seawater DIC and macroalgal biomass within replicate F. vesiculosus and M. pyrifera kelp forests; this will be integrated with measurements of carbon use efficiency for key macroalgal species to determine the extent and timing of C limitation in these kelp forests in situ. We will identify groups of species likely to respond differently to the changing chemical speciation of DIC predicted over the next 100 years and develop models of community level responses to elevated CO2. Laboratory experiments on functional groups of macroalgae under controlled DIC and light will test our hypothesis that: 1) the physiological and growth responses of macroalgae with different carbon uptake mechanisms will differ under changing DIC availability and speciation; and, 2) the availability of CO2, an inexpensive C source for photosynthesis in seawater, will limit photosynthesis and productivity during periods of optimal growth in dense kelp forests. Our data will inform predictions on how kelp forests could buffer against ocean acidification and how increasing CO2 could influence the cultural and economic values that kelp forests provide (estimated to be worth billions of dollars annually). This information is critical in providing realistic forecasts of future marine productivity and carbon sequestration by coastal primary producers. Additionally, understanding DIC limitations in M. pyrifera and F. vesiculosus dominated communities will help inform the conditions for aquaculture where productivity is optimal. Therefore, providing valuable information regarding limiting resources (in this case, carbon) to interested stakeholders who wish to develop kelp aquaculture and/or integrated multi-trophic aquaculture.

Supervisors: Kadri Koorem, Siim-Kaarel Sepp

Increasing human activity leads to novel ecosystems – combinations of biotic communities and abiotic conditions that have never co-occurred in evolutionary time. Plants are the primary producers of terrestrial ecosystems and form an essential part of these novel ecosystems. With the number of plant species establishing outside their native range increasing and biodiversity decreasing due to increasing human pressure, it becomes crucial to improve the current understanding of how and why non-native plant species structure the communities of plant-associated organisms. This PhD project will use a new metric, the Biotic Novelty Index, to explain the diversity of plant-associated insects (herbivores) and soil organisms (pathogenic and mutualistic fungi). The Biotic Novelty Index combines the traits of non-native and resident plant species with their time of coexistence, which are expected to explain functional differences between nonnative and resident plant species. The PhD project will utilise data from an ongoing global network in parallel with a greenhouse experiment. The results of this PhD project will improve existing knowledge about the effects of the invasion of non-native plant species on multiple trophic levels under current and global change scenarios. These results will enable the detection of the communities most vulnerable to invasion from a multi-trophic perspective.

Supervisors: Aveliina Helm, Polina Degtjarenko

The world is becoming increasingly urbanized. The United Nations reports assess that if current trends continue, there will be nearly 6.8 billion people living in cities by 2050, which is half as many as in 2010. Increase of urban areas is expected to continue also in Estonia. Urbanization has strong impacts on biodiversity, both via its direct influence on habitat cover and quality, but also via changes in people's social and cultural relationships with the natural environment. Conservation and restoration of biodiversity and well-functioning ecosystems is crucial in urban areas to tackle biodiversity loss, create good living environment in cities, and support people's exposure to biodiverse nature. The aim of the doctoral thesis is to develop the most effective methods for the ecological restoration of the species-poor and ecologically impoverished green areas in Estonian urban areas. Thesis will analyze the effectiveness of different interventions that have been carried out to improve biodiversity condition in urban areas so far. In addition, thesis will explore the role of Estonian seminatural grasslands as possible target ecosystems to ecological restoration urban green areas. By comparing the ecological parameters of semi-natural grasslands (richness and composition of different species groups, functional diversity and composition, provision of different ecosystem services) and urban ecosystems, it is possible to identify the most beneficial targets for ecological restoration of urban green areas. Knowledge from semi-natural grassland ecosystems can also inform regarding the most suitable maintenance-restoration techniques for increasing the biodiversity of the urban environment. Within the framework of the project, recommendations and best practice guidelines will be developed for increasing the biodiversity of green areas in Estonian cities, including the most suitable 'standard' species compositions and communities for spatial planners to be used in their work.

Supervisors: Dr Inga Hiiesalu, Dr Tanel Vahter, Prof. Maarja Öpik

The aim of the proposed PhD project is to significantly advance the knowledge on how diversity and functioning of farmland soil biota, and specifically arbuscular mycorrhizal fungi (AMF), is influenced by management, and how to protect, manage and use soil biota. Specifically, the aim of the thesis is to map AMF diversity in Estonian farming landscapes and link this with underlying soil functions. The PhD project will combine available data on AMF occurrence with newly collected data from across Estonian fields where different management techniques have been used. In addition, the doctoral project will also utilize several large-scale experimental setups in Estonia by adding a complementary, in-depth analysis of AMF diversity within these existing field experiments. To establish and assess the link between AMF diversity, biomass and soil functions, the PhD project will focus on the AMF-related functions in soil nutrient cycling, specifically greenhouse gas emissions and leaching. These aspects will be tested in a greenhouse experiment, which will provide a more mechanistic understanding of the underlying processes. Finally, the data generated in the thesis will form the basis for developing a novel decision tool of mitigation strategies for specific environmental goals and regional contexts. With global change having an ever-increasing impact on farming, tools like this have become a necessity to ensure the capacity of our landscapes to provide both food and ecological functions simultaneously, while still being profitable for the farmers.

Supervisors: Tiit Teder, Toomas Esperk, Sille Holm

The effect of crowding – atypically high densities of juveniles – has been reported in several fundamental life history traits such as body size, fecundity and dispersal ability. Nevertheless, it remains unknown if these effects are widespread and similar across the phylogenetic tree of insects, and to which extent are crowding effects adaptive. The proposed doctoral project will serve to fill this gap by exploring the phylogenetic and ecological/evolutionary background of insect responses to high juvenile densities with the additional aim of providing input for commercial insect rearing. More specifically, the following research questions will be addressed. Firstly, a meta-analysis of the reported crowding effects in insects will be carried out to clarify how widespread and similar the effects are across insect taxa. Next, available information on (optimal) population densities in commercial rearing of insects will be reviewed. The third study will evaluate phylogenetic signal in crowding effects to determine the level of phylogenetic conservatism. The fourth study will determine the factors eliciting crowding effects in commercial insect rearing, using black soldier fly (Hermetia illucens) commonly reared for food and feed. The project is expected to contribute to basic life history theory by focusing on plasticity in fundamental fitness-related traits, while also having direct applied significance for commercial rearing of insects.

Supervisors: Kadri Runnel, PhD; Mikk Espenberg, PhD

About 10% of the global terrestrial carbon pool is stored in dead wood in forest ecosystems. Decomposition is the main pathway by which carbon in dead wood returns to the atmosphere. Developing and refining our mechanistic understanding of decomposition is, therefore, the key to predict carbon store and flux from dead wood. The frame of this PhD is a larger project, which aims to describe, explain and predict where and why dead wood decomposes slowly in nature, and to develop nature-based solutions for maximizing carbon storage in dead wood. During your PhD studies within this project you will (1) develop and test an innovative new method for in situ measurement of the gas emissions from dead wood (based on an existing blueprint), and (2) study the contribution of decomposer (fungal) assemblages and the environment in explaining dead wood decomposition rates. The study will be conducted in hemiboreal forests in Estonia, where a set of natural dead wood items will be studied for mass loss, fungal communities, and gas emissions.

Supervisors: Urmas Saarma, Ants Tull, Harri Valdmann

Parasites have an important role in both animal and human health, causing diseases and mortality. It is known that the vast majority of parasites are zoonotic and therefore it is important to collect information about their occurrence and transmission routes. Parasite studies in wild animals often rely on sampling a single group of parasites within a limited time range. As a result, our understanding of the temporal variation of a wide range of zoonotic parasites remains poorly resolved. The present study aims to analyse a wide range of zoonotic parasites in two major carnivore parasite spreaders in Estonia – the red fox (Vulpes vulpes) and raccoon dog (Nyctereutes procyonoides) – throughout a year and in three consecutive years. Both molecular and morphological methods will be used, including metabarcoding to identify a large spectrum of different parasites belonging to cestodes, nematodes, trematodes and protists. Morphological analysis of food items consumed by the animal will be also performed to understand relations of diet and parasite prevalence/transmission. Based on the collected data we will be able to gain a novel insight into the temporal changes of different zoonotic parasites and factors important for parasite transmission.

Supervisors: Niloufar Hagh Doust, Leho Tedersoo

The doctoral student is admitted to a student's place of study, full-time, the nominal study period is four years. An employment contract for a junior researcher is not concluded. The planned start of study and research is September 4, 2023.

Plant-associated microbes are key drivers of biodiversity and functionality in plant communities worldwide. Underground plant-associated microbes such as mycorrhizal fungi have drawn the attention of scientists for decades, but less is known about the aboveground plant-associated microorganisms. The aboveground organs of plants host numerous ecologically important fungi, yet diversity patterns and the spatial distribution of such fungi remain unknown. Although the host-specificity of rootassociated fungi has been studied profoundly, it is still unclear to what extent the leaf fungal communities are linked to different plant species. The recent development of high-throughput sequencing methods has allowed quick and more thorough characterization of fungal communities from various ecosystems and substrates. The main aim of this Ph.D. project is to study the host-specificity and spatial patterns of leaf-associated fungal communities in Europe. In particular, we ask I) how the structure and diversity of different groups of fungi change along latitudinal gradients; II) what are the most important environmental factors shaping these communities at small and large spatial scales. In addition, we will investigate the host-specificity patterns of different groups of fungi in trees and understory vegetation. This Ph.D. thesis relies on samples that have already been collected in the frameworks of “FunLeaf” citizen science campaign, and fungal communities will be described using up-to-date molecular tools. We will use “FungalTrait” and “Funguild” databases to assign OTUs to different functional traits and model-based statistics to investigate their distributions and diversity patterns. This project will significantly improve our fundamental knowledge of leaf-associated fungal communities.

Supervisor: Leho Tedersoo

The doctoral student is admitted to a student's place of study, full-time, the nominal study period is four years. An employment contract for a junior researcher is not concluded. The planned start of study and research is September 4, 2023.

Fungi form diverse communities in forest ecosystems, with apparent specialisation on decomposing different substrates, attacking or nourishing different plant species. However, at the ecosystem level, there is no holistic understanding to what extent different habitats harbour unique fungi and to what extent the communities overlap across these microniches. This project aims to determine 1. microhabitat specificity/preference in fungal species; 2. relative host tree specificity of fungi in living and dead plant tissues; and 3. the effect of sampling and sample pooliing strategies on the efficiency of recovering fungal diversity. The project uses cuttingedge molecular methods for identification, bioinformatics and statistics. The potential importance is very high from the microbial ecology perspective, because knowledge about the relative microniche and host preferences of fungi enable to understand how broad are fungal niches and hoe this can be used in the conservation perspective.

Supervisors: Maarja Öpik, Ivika Ostonen-Märtin, Saoirse Tracy (University College Dublin)

The PhD project, which is part of Marie Curie Doctoral Network ROOTED, aims at monitoring root system architecture in relation to root-symbiotic arbuscular mycorrhizal (AM) fungal performance (diversity, composition, abundance, activity) in the context of indigenous and introduced (inocula) AM fungi and under different agricultural practices in different areas. We hypothesise that different interaction characteristics occur between the indigenous and introduced (inocula) into the plant: soil environments. Fundamental new knowledge will be gained on root-symbiotic arbuscular mycorrhiza under various agricultural practices in different areas.

Seealso here: https://euraxess.ec.europa.eu/jobs/84258

Supervisors: Koit Herodes, Asko Laaniste, Ivo Leito

Goal of the project is to develop a modern approach to analytical method validation with proof of concept software. It will rely upon and further develop the validation software ValChrom (valchrom.ut.ee). The outcome will be a published concept to automate validation process using the combined interdisciplinary strength of chemistry, software development and complex statistical approaches (design of experiments). PhD candidate will pull together these resources and systematize a modern approach on how to bring the validation of analytical methods out from the currently largely error-prone and cumbersome spreadsheet based software approach into automated and statistically sound approach. This will assume a high level of interdisciplinary knowledge handling and also familiarity with software development.

Supervisors: Rasmus Palm, Enn Lust, Thomas Thomberg, Angélica María Baena

Moncada Metal-organic frameworks (MOFs) include a wide variety of meso- and microporous structures with the potential to incorporate different metals and heteroatoms. Carbonisation of MOFs yields highly porous carbon materials with uniformly distributed catalytically active functional centres. The aim of this PhD project is to synthesize and carbonise MOFs yielding highly functional catalytically active model carbon catalyst materials. The MOF derived materials will be used to investigate the effect of different active sites in a carbon material to the adsorbate/adsorbent interactions important for energy storage and energy conversion applications. The focus of this PhD project will be to synthesise, carbonise, and perform physical characterisation of MOF-based materials. This will include the optimisation of the MOF synthesis and carbonisation routines to obtain highly porous materials with welltuned highly accessible active sites. The fine-tuned active sites would be of interest for the investigation of hydrogen adsorption and hydrogen spillover effect, as a catalytical support for nanoconfined hydrides, and as a catalytic site for oxygen reduction reaction. The PhD project will involve the physical characterisation of the prepared materials with various methods like gas adsorption, X-ray diffraction, and Raman spectroscopy. In addition, neutron scattering experiments will be performed to investigate the structure and processes of interest for energy storage and conversion applications on these materials.

Supervisor: Ivar Zekker

In order to prevent eutrophication of water bodies, it is necessary to reduce the amount of phosphorus and nitrogen in wastewater, and before discharging into the water body, the wastewater must be purified of pharmaceutical residues, so that bacteria/viruses do not develop drug resistance. The removal of sartans, tramadol, venlafaxine, cabapentin, CIP, NOR, OFL, SDM and SMX by autotrophic organisms will be investigated in biofilm and suspended sludge systems and in bioelectrochemical systems. The study of anaerobic ammonium oxidation in the treatment of pharmaceutical residues is necessary, because it enables the saving of aeration energy, thus being about 50% cheaper than technologies that only use aerobic, heterotrophic organisms. In addition, there is a great need to investigate the removal and inhibition of pharmaceutical residues and metals due to the increasingly strict requirements set by HELCOM for nitrogen limit values in wastewater treatment plants to prevent eutrophication and toxicity in the Baltic Sea region.

Supervisors: Srinu Akula, Kaido Tammeveski

Proton exchange membrane fuel cells and anion exchange membranes fuel cells are important energy conversion devices due to their high efficiency. Pt-based catalysts and their alloys supported on nanocarbons are typically used to catalyze the oxygen reduction reaction (ORR). However, scarcity, poor stability, high cost are still main barriers towards the commercialization of fuel cells. Therefore, several approaches have been employed to improve the ORR electrocatalytic activity and fuel cell performance. The transition metalnitrogen-carbon (M-N-C, M = Fe, Co, Ni, Mn, Cu, etc.) single-atom catalysts are new frontiers in the field of electrocatalysis for fuel cells due to the maximum metal atom use and high electrocatalytic activity. Hence, suitably engineered single-atom catalysts and porous metal-organic frameworks play crucial roles in improving the performance of these materials. Heteroatom (N, B, F, P, S) doping of the carbon nanomaterials influences the electronic properties that are favorable to ORR. This electrocatalytic effect can be enlarged further by creating the M-Nx coordination environment with the single metal atom cites. The synthesis conditions will be optimized to prepare highly active catalysts. A thorough electrochemical and physical characterization of the catalyst materials will be carried out in the frame of the PhD studies. The accelerated durability testing will be also conducted. Polymer electrolyte fuel cell performance will be evaluated using the best electrocatalysts as cathode materials.

Supervisors: Margus Kanarik, Jaanus Harro

The long-term activity of brain regions and networks is reflected in the function of cellular respiratory chain enzymes, most prominently in the activity of cytochrome c oxidase. We use different animal models to determine which areas of brain activity and which patterns of functional connectivity of various brain areas are involved in vulnerability and resilience to psychiatric disorders. In the doctoral project the metabolic activity of the brain will be mapped in different mouse and rat models via cytochrome c oxidase histochemistry, the method that allows detecting long-term activity patterns of specific brain regions and networks. The rodent models that will be included in the doctoral project will be: Tph2-deficient mice with compromised serotonin synthesis; rats whose inherent exploratory phenotype was changed from low to high via intracerebral GDNF administration; the effect of compounds that regulate RNA methylation on behaviour; and the effect of positive affect and resilience to stress, as measured via emission of 50 kHz ultrasound vocalisations. The doctoral project will investigate how the brain is impacted by different conditions connected to brain monoamine systems. This is important to advance our knowledge and to localize distinct brain regions that differentiate the long-term metabolic activity of vulnerable and resilient animals.

Supervisor: Nadezda Kongi

This PhD project aims to address the urgent need to mitigate the threat of climate change by focusing on the synthesis and characterization of electrocapture materials for use in electrochemistry-based Direct Air Capture (DAC) processes. The project will use a multi-disciplinary approach combining density functional theory (DFT), artificial intelligence (AI), materials science, and electrochemistry to design, synthesize, and optimize new electrocapture materials with high CO₂ affinity and selectivity. The expected outcomes include the development of new materials and improved understanding of their electrochemical properties, with the potential to revolutionize the field of atmospheric CO₂ removal.

Supervisor: Ivo Leito

The PhD project will carry out a large-scale investigaton of acidity (pKa values) of carboxylic acids (around 100 acids) in different non-aqueous solvents (DMSO, acetonitrile, dimethyl formamide, propylene carbonate and possibly others) and biphasic octanol : water system. The results will be used for composing prediction models of carboxylic acid strengths and revisiting the question of the origin of strength of carboxylic acids in different solvents.

Supervisors: Gholamreza Anbarjafari and Chagri Ozchinar

Small and tiny object recognition is a challenge that has become more important due to rapid development in AI based security tool development. In this PhD we are focusing on recognition of tiny objects in 2D and 3D using single shot learning. We will investigate Multiview analysis for 3D objects to enable late fusion in the recognition pipeline. 3D objects will be presented in pointcloud form which will be produced through the project SilentBorder. The developed deep neural networks will be subject to network optimization to assure the developed solutions can be used in the real-time scenarios.

Supervisor: Karl Kruusamäe

In Industry 5.0 robots plays a crucial role in relieving workers from repetitive, unhealthy or dangerous tasks by leveraging human-robot collaboration (HRC). Two high impact areas that seek to benefit from HRC are manufacturing (i.e. lessening the physical demand on workers) and waste management (i.e. keeping people from potentially hazardous materials, including nuclear waste). By addressing these application domains for achieving safe human-robot interaction, one targets the key scenarios in safe HRI: 1) co-located HRC and 2) userexperience during remote telerobotics. In this thesis, the aim is to develop a shared autonomy human-robot system that leverages augmented reality, digital twins, and deep reinforcement learning for safe human-robot interaction with potential use-cases in manufacturing and waste management.

Supervisors: Karl Kruusamäe, Veiko Vunder

Open source hardware (OSHW) stands for machines, devices, or other physical things whose design has been made public in such a way that anyone can make, modify, distribute, and use those things. Inspired by the success of open source software and the availability of technologies, such as 3D printing, the concept of OSHW holds tremendous potential in liberating the technology for widespread access and thus even contributing to achieving the global UN sustainable development goals. However, compared to open-source software, the OSHW is still at its infancy where best practices require further elaboration and validation before success stories with global impact emerge. The objective of this thesis is to develop and validate a development process of open hardware in the context of robotic platforms. As an outcome of the thesis an OSHW model for continuous robotic development and integration is proposed. The model is validated for at least two different robot architectures.

Supervisors: Kaur Alasoo

Gene expression quantitative trait loci (eQTL) studies are a powerful tool to link common genetic variants associated with complex traits to their causal target genes. Although these variants impact gene expression at the level of single cells, the vast majority of eQTL studies have been performed either in bulk tissues or heterogeneous cell populations. Recently, seven high quality single-cell eQTL (sc-eQTL) studies have been published from several human tissues and cell types. These seven studies have collectively profiled over 5.8 million cells from >2000 individuals across multiple ancestral groups and have made all their data available for re-use for qualified researchers. However, a major bottleneck is the lack of automated and reproducible computational workflows to enable the processing and integration of these terabyte-scale datasets at scale. In this project, the student will first develop computational workflows for efficient and reproducible sc-eQTL data processing. They will then use the uniformly processed data from seven studies to systematically characterise conditionally independent genetic variants associated with gene expression levels in major human immune cell types. Finally, the student will explore deep learning model to integrate these diverse datasets into a single latent space and identify genetic variants associated with latent dimensions of the data. The results of this project will greatly improve our understanding of how genetic effects on gene expression vary between human immune cells and how this information can be used to prioritise both causal genes as well as cell types for complex human traits.

Supervisors: Raivo Kolde

Increasing availability of real-world healthcare data creates a need to develop novel computational methods for analysing it. Particularly, such data allows deep characterization and discovery of real-world clinical event sequences, also known as clinical or disease trajectories and pathways. Improving the understanding of clinical trajectories allows to make better public health and regulatory decisions, and improve quality of treatment.  There is considerable amount of research towards these goals, but the methods used are ad hoc, making comparability and reproducibility of the results low. The goal of the project is to develop computational methods for systematic characterization of the existing treatment patterns, comparison of those to expectation and discovery of novel disease pathways. The method development will be carried out in parallel with running local and international clinical studies employing the methods.

Supervisor: Meelis Kull

Masinõppel põhinevad tajusüsteemid on muutumas üha täpsemaks maailma modelleerimisel, kuid suure eraldusvõimega piltide töötlemiseks vajalik arvutusmaht on muutumas reaalajas praktiliste rakenduste kitsaskohaks. Selle ületamiseks võib tajusüsteem hajutada arvutusi mitme kaadri vahel, kasutades tähelepanumehhanismi. Selline lähenemine tekitab aga uusi väljakutseid määramatuse kvantifitseerimisel, mis nõuab uusi meetodeid uue teabe haldamise ja eelneva teabe vananemise juhtimiseks. Kavandatava doktoriprojekti eesmärk on arendada neid uusi meetodeid ning aidata kaasa arvutusressursside vähendamisele ja paremale määramatuse hindamisele.

Supervisors: Stefania Tomasiello, Radwa El Shawi

Precision mental healthcare is an emerging field. Existing research showed the potential of machine learning for predicting treatment outcomes and dropping-out cases. The problem of generalization with a limited number of samples and unbalanced data has not been addressed yet. In the same context, studies on approaches processing multimodal data to enhance the prediction using different sources (e.g. imagery, texts) are also missing. Improved robust techniques will be designed to this end, taking into account any possible uncertainty in data. As expected in fields such as medicine, these techniques will also be interpretable. The newly developed techniques will be tested on some noticeable benchmarks presented in the literature and on a real-world dataset.

Supervisor: Roshni Chakraborty and Rajesh Sharma

There has been drastic shift in news consumption behaviour from printed news media to online news media. Specially these days users rely on both social media websites, such as, Twitter, and online news media websites (yahoo and google news) for news. In order to retain the user’s attention, news media agencies provide continuous streaming of news, recommend and filter news specifically for users on the basis of their news consumption history and their social circle. Therefore, the news shown to different users varies on the basis of the topic, type of news and even, the perspective of the same news. On the downside, this segregates the users on the basis of their ideological, political, democratic and other implicit characteristics which leads to the formation of echo-chambers. This can invariably increases polarization in society, affect the quality of daily life and increases misinformation spread. Therefore, in this PhD work, we propose automated approaches to identify bias in news media and subsequently formation of bubbles of users. Additionally, we explore the impact of the bias in news media on polarization in social media and misinformation spread, and provide solutions both at user and news media level to resolve these. To achieve this, we will propose scalable computational techniques based social network theories, natural language processing, AI and data science.

Supervisors: Dietmar Pfahl, Faiz Ali Shah

The creation of data sets that can be used to test newly developed software applications by service consumers who plan to use services provided via the Estonian X-tee data exchange layer are difficult for several reasons. Firstly, to not violate privacy preservation and security requirements, it must be guaranteed that the dataset used for testing cannot be reverse engineered such that elements of the real-world dataset are identifiable. Secondly, the dataset used for testing must be similar enough to the real-world dataset with regards to properties such as form, structure, update frequency for a large range of event types, and evolution patterns over time.

To address the challenges, the goal of this thesis project is to explore, combine, and enhance existing methods for synthetic test data generation, such as microdata release approaches and intelligent, machine learning based approaches. The new method will be evaluated in use cases related to the Estonian X-tee data exchange layer and will be made reusable such that it can be used in similar other contexts by industry and public entities.

Supervisor: Dmytro Fishman

In our project, we aim to tackle the challenge of limited annotated data for training deep neural networks in biomedical imaging, particularly in 3D imaging, such as computed tomography (CT) scans, magnetic resonance images (MRI), 3D ultrasounds, etc. Significant time and resources are invested globally to annotate sufficient data for deep learning models. Although some annotated data is publicly available, it's often too broad for projects focused on a specific area, such as segmenting spleen cancer in CT scans. However, research has shown that models can achieve adequate performance with just a fraction of annotated pixels. This has been explored in 2D imaging, but not yet in 3D medical imaging.

We believe that utilizing incomplete annotations is a promising approach for 3D medical images. There are various sources of partial annotations in the field, such as measurements from radiologists on CT scans, or biologist-provided inputs. Our project's unique contribution is applying this concept to 3D medical images, reducing the time spent on annotation while still yielding comparable results to models trained on complete labels. We plan to use users' partial annotations, collected either retrospectively or prospectively, to train our models. In the latter case, we will implement a model-based guidance system to direct users to the most relevant regions for annotation. We will compare the results of models trained on partial annotations to those trained on complete annotations to gauge the benefits. This project will be carried out in collaboration with industrial partners that will contribute data and funding.

Supervisors: Radwa El Shawi, Stefania Tomasiello

Industry applications of machine learning on streaming data have received great popularity over the last few years due to the increasing adoption of real-time streaming in IoT, microservices architectures, web analytics, and other areas. However, the current offline AutoML frameworks assume that the entire training dataset is fixed in size and available upfront and that the underlying data distribution is almost fixed over time. These assumptions are not valid in a data-stream setting where the data is an unbounded stream and cannot be stored completely. The goal of this doctoral work is to introduce an explainable automated supervised framework for online AutoML. Such framework should empower users to develop satisfying and trusted models and reduce the burden on data scientists and domain experts for going through the time-consuming process of building and deploying machine learning models.

Supervisor: Marlon Dumas

In the field of Business Process Management (BPM), the problem of resource optimization is that of finding a set of resources to perform work in a business process, in a way that optimizes one or more performance measures.

This doctoral project will address the problem of resource optimization with respect to two performance measures: cost and cycle time.

This optimization problem has been widely studied in the field of BPM. However, existing approaches make restrictive assumptions about the behaviour of resources. For example, existing methods assume that a resource always exhibits the same performance. However, the performance of a human resource may be affected by fatigue and stress. Similarly, existing methods assume that if an activity instance is ready to be performed and a resource is available, the resource will immediately start working on it. However, a human resource might not start the activity immediately because of a distraction, an external interference (e.g. handling an urgent email) or because they prefer to organize their work in batches.

This doctoral project will develop business process optimization methods that do not make such unrealistic assumptions about the behavior of resources in a process.

To achieve this goal, the doctoral project will combine methods from probabilistic modelling, machine learning, business process simulation, process mining, and optimization algorithms.

Supervisors: Kairit Sirts, Andero Uusberg

Developing AI-based self-help systems for common mental health problems can have a great potential in complementing or, in milder cases, replacing the psychotherapy with a human therapist. Such systems could help people detect and correct their cognitive distortions, which are patterns of biased information processing often exhibited by people suffering from depression or anxiety, based on textual journal entries. Previous research has attempted to recast cognitive distortions in text in a less biased way with modern text generation models. However,  instead of automatic reformulation, the user of a self-help system needs specific instructions on how to reformulate their thoughts. The goal of this project is to develop methods based on natural language processing to generate specific instructions based on user texts, which would help the user to reformulate their thoughts in a less distorted way. For this purpose, automatic predictions are used about the patterns of cognitive distortions present in the texts and the evaluations of the reappraisal dimensions of emotions. The expected result of the project is a model that generates feedback instructions, on the basis of which a preliminary prototype of a mental health self-help system could be created.

Supervisors: Huber Flores

Humans tend to spend most of their life indoors making the quality of indoor environments essential for human health and well-being. While several solutions for monitoring the indoor environment have been proposed, ranging from infrastructure-based monitoring solutions to cameras, these tend to require static installation making them difficult to adjust to seasonal requirements and to collect high spatial and temporal indoor data. In this project, we introduce the idea of using smart plants as an easy-to-deploy and affordable solution for monitoring the indoor environment. These plants are further integrated into autonomous ground places, such that they can easily be re-located and adjusted to indoor characteristics that improve the quality of the environment autonomously.

Supervisors: Assoc. Prof. Amnir Hadachi (UT) and Assoc. Prof. Chaoru Lu (OsloMet)

Over the last few years, the smart city concept and sustainability have been seen as the way to solve our urbanization rise issues and ensure quality of life, comfort, safety, good governance, and a good atmosphere. In this concept, smart infrastructure is inspirational in its major vital components. Moreover, all its elements, such as Smart Mobility, Smart energy grid, and Smart transport, are connected, generating information and knowledge about each other that can be used to reach the optimum strategy for resource management and improve performance. Hence, our proposal behind this thesis topic is to innovate in the direction of using the latest trend solution in smart mobility – Micromobility vehicles – as a sensor to build a smart AI solution following the “Green Learning Paradigm” for monitoring and assessing the infrastructure based on the interaction between means of transportation and the urban road network (including sidewalks).

Supervisors: Vesal Vojdani

This doctoral project focuses on developing truthful and explainable program analysis methods. The goal is to produce insightful and semantically connected explanations that are guaranteed to be truthful about the behavior of software systems. The research plan includes building on existing work and focusing on generating truthful explanations using Interactive Abstract Interpretation, Sound Warning Repositioning, and Thread-Modular Meta-Analysis. The main novelty of this project is the guarantee of truthfulness for the end-user output.

Supervisors: Pelle Jakovits

The goal of this thesis is to propose a framework for accurate data collection, fingerprinting, and lineage tracing in IoT and other data generated in production supply chains. The focus is on the wood supply chain, where the origin of the material must be tracked and proven in accordance with the European Renewable Energy Directive II (RED II) and the upcoming RED III directive. The proposed framework will use advanced data engineering techniques, blockchain, anomaly detection, NLP, and association rule mining to gather and analyze forest supply chain data.

The thesis will propose novel ways to improve data traceability, including fingerprinting, data fusion, inverse prediction, and origin prediction confidence calculation. To address privacy and security issues, the thesis will explore ways to use blockchain and encryption technologies. The ultimate goal is to create a methodology to objectively measure upstream materials' traceability based on the data's accuracy and availability, thereby avoiding "Green Washing". The framework will be designed to be easily generalized to other industries and improve data gathering through various methods, such as data fusion and satellite imagery.

Supervisors: Kaur Alasoo

Drug target prioritizing based on genome wide association studies (GWAS) involves three major parts: 1) identifying the mode-of-action by which the causal variant affects the phenotype (changes in either coding DNA, expression or splicing), 2) identifying the correct target gene, and 3) identifying the disease-relevant contexts in which the target is active. Coding changes explain only a small fraction of the GWAS signals. Variants regulating gene expression (eQTLs) explain many genotype-disease associations, but most of them are highly pleiotropic and thus problematic for prioritizing specific targets. In contrast, recent evidence has demonstrated that variants regulating splicing (sQTLs) are much more specific and thus better suited for identifying causal target genes. The aim of this PhD project is to develop data visualisation tools and analysis methods to better describe and predict genetic variant involved in the regulation of RNA splicing.

Supervisor: Kuldar Taveter

Many large IT projects fail because of user resistance, which is, in turn, caused by insufficient involvement of users and other stakeholders in the projects. The PhD student candidate has experienced the lack of user involvement in different public sector IT projects carried out in Estonia. Typically, such projects delve into detailed business process modelling at their very early stages, which rapidly increases complexity and decreases the user involvement. To improve the situation, this doctoral project aims to put forward a scientifically justified and repeatable method of conducting large-scale IT projects with substantial and systematic stakeholder involvement. To achieve that, we propose to look at the large-scale IT projects through the lenses of sociotechnical systems (STS), which treats an organization as consisting of interacting humans and technical components. We plan to view an organization as STS from the complementary organizational, informational and functional perspectives. For eliciting and representing the knowledge required by the three perspectives, we proceed from the method of motivational modelling that elicits the requirements through physical or virtual do/be/feel stakeholder workshops. The workshops result in intuitive and lightweight models of requirements, such as goal models for the functional perspective, which represent functional, quality and emotional requirements. We aim to find out what is the impact of applying such structured methods of stakeholder involvement on user involvement and user resistance and how the do/be/feel method could be complemented for the elicitation of requirements from the organizational and informational perspectives in addition to the functional perspective that is covered by goal models. We will also explore how the resulting high-level requirements could be semi-automatically transformed into business process models. To answer the research questions, we will iteratively develop two artefacts – a methodology and a tool for systems analysts supporting the methodology – by applying the research method of design science. We will validate the method in three or four projects to be carried out in the Estonian public sector and in the international project working out an ecosystem of IT solutions for older adults.

Supervisors: Raimundas Matulevičius, Mubashar Iqbal

The intelligent infrastructure has a lot of specifics and challenges - arising from collecting and storing data from a large number of devices, temporal communication among them, security, privacy protection, and many others. Such challenges stand as the primary motivation for implementing blockchain in intelligent infrastructure. The blockchain-based intelligent infrastructure introduces diverse characteristics that enable secure and transparent sharing of information and data between different system components and stakeholders, ultimately improving the intelligent infrastructure's efficiency, reliability, and security. However, the existing blockchain types and their implementations provide dissimilar ecosystems, system settings, and consensus rules resulting in various tradeoffs between the offered blockchain characteristics. This research would consider blockchain-enabled use cases primarily targeting blockchain-based intelligent infrastructure for seamless, secure, and trusted data transmission between components of intelligent infrastructure.

Supervisors: Eduard Shevtshenko, Ibrahim Oluwole Raji (Middlesex university)

Due to the rapid growth of e-commerce in recent years and its effect on the increase in the volume of mail and courier transport, the need has arisen for the dynamic optimization of logistics routes. Solutions have been developed around the world that can calculate the optimal parcel transport route between geographical coordinates. The existing applications do not take into account the changing volumes of parcel transport, times of day, weight and dimensions of special types of parcels, traffic, the number of stops, different addresses of the same person in the business-to-consumer direction, maximizing the use of cargo cubic capacity, under the conditions of constant working hours of the service provider.

 The aim of the research work is to develop a dynamic package routing algorithm that can be interfaced with special types of existing postal logistics information systems. Then implement and validate the algorithm as a component of Eesti Post's information system. In addition, the developed algorithm provides input for the calculation of dynamic parcel transport pricing.

Research is of critical importance to “Eesti Post” and society in general, in order to reduce the costs of mail and parcel transport, minimize fluctuations in workflow and optimize the use of resources.

Supervisor: Dominique Unruh

The security of cryptographic systems is normally ensured by mathematical proofs. Due to human error, however, these proofs often contain errors, limiting the usefulness of said proofs. This is especially true in the case of quantum protocols since human intuition is well-adapted to the classical world, but not to quantum mechanics. To resolve this problem, we need methods for verifying cryptographic security proofs using computers (i.e., for “certifying” the security).

The goal of this thesis is to: (a) Formalize post-quantum security proofs of relevant cryptosystems with computer-verified proofs using tools developed in our group. (b) Develop new automation techniques for doing such security proofs and to use them to improve the verification tools.

Supervisor: Rajesh Sharma

The presence of Misinformation on Online social media has become a menace. Timely detection of misinformation is essential as the spread of false news can have adversarial effects. For example, it can 1) trigger riots, 2) loss in the financial market and, 3) affect elections’ outcome. Traditionally, the main focus of researchers has been on proposing state of the art AI based solutions which can detect misinformation. On the other side, less focus has been made on studying users who are often involved in various kinds of misinformation, that is, fake news, rumours, etc. One way to move forward is to exploit the social relations (for example, friendship on Facebook and follower-followee relations on Twitter) among the users of online platforms involved in posting misinformation. In this PhD project, we plan to perform various kinds of user studies which are involved in spreading misinformation using various kinds of data science techniques. These techniques include social network analysis, and natural language processing. The user studies will help in making even predictive models better and ultimately demystify the reasons and process of misinformation diffusion.

Supervisor: Sulev Reisberg

During the provision of healthcare services, large amounts of healthcare data is collected. This has led to increased interest in utilizing this data for secondary use and research purposes. One area of focus is the automated identification of clinical event sequences in the data, which can provide insight into disease trajectories - that is, to reveal the causes and progression of the disease, as well as treatment options and effectiveness. However, current methods for identifying these trajectories have been limited in their effectiveness. The goal of this research project is to address these limitations by developing and improving upon existing analysis methods and evaluating their applicability to diverse healthcare datasets in Estonia and internationally. Through this work, we aim to gain a deeper understanding of disease progression and inform the development of more effective treatment strategies.

Supervisor: Naveed Muhammad

Localization, in the context of autonomous driving, is the ability of a vehicle to estimate its position and orientation, along with an estimate of uncertainty. Autonomous vehicles rely on centimetre-accuracy GNSS (e.g. GPS) for localization. Map-based localization techniques exist for autonomous vehicles, but they have only been investigated for limited geographical areas and mild, consistent weather conditions. For true autonomy, fully autonomous vehicles of the future need to be able to autonomously localize themselves without complete dependence on GNSS, to be able to cope with GNSS unavailability and sensor failures. Advances in environment perception using deep learning have opened up the possibility of overcoming the challenges involved in GNSS-free map-based localization over extended areas and varying weather conditions. The proposed project aims to investigate identification and use of structural features from the environment that are robust to varying weathers. Such features will then be employed for map-based localization using particle filtering. This also includes a comparison of high-definition and Open Street Maps for map-based localization. Expected outcome of the projects includes feature definitions and algorithms for mapbased localization on a national scale and varying weathers. Such methods will be a significant contribution towards level-5 autonomous driving, especially as machinelearning based “end-to-end” approaches in autonomous driving mature.

Supervisors: Amnir Hadachi, Abdelaziz Bensrhair, Paul Honeine

In the last decades, we witnessed rapid artificial intelligence advancements built upon deep learning (DL). Moreover, the DL decision mechanism is so obscure that testing is the only way to verify it. Hence, the process from training to testing any model is computationally demanding. Consequently, due to their high carbon footprint, DL networks become a concern for suitability. From this perspective, green learning (GL) has been presented as a potential solution to address these concerns. Thus, the Ph.D. topic is focused on exploring the possibilities of the GL paradigm and how it can be adopted in rethinking and redesigning the models’ architectures to reduce the carbon footprint of computer vision algorithms based on Deep learning.

Supervisor: Hanna Hõrak, Ebe Merilo, Reine Koppel

Stomatal pores in leaves mediate CO₂ uptake for photosynthesis and water loss via transpiration; they also act as entry points for many plant pathogens. Thus, stomatal numbers and apertures are important determinants of plant stress tolerance, disease resistance and yield. During climate change, air becomes drier and hotter: both air temperature and vapour pressure deficit (VPD, the difference between air humidity and saturating air humidity) will increase. These changes negatively impact on plant stomatal traits and decrease crop yields. Wheat is one of the most important cereal crops, but gaps remain in understanding how future climate conditions affect wheat stomatal traits, disease resistance and yield. The aim of the project is to understand the relationships of wheat stomatal traits with disease resistance and yield, and test, how elevated temperature and VPD affect wheat gene expression, stomatal anatomy and physiology. The project will contribute to the identification of stomatal traits that are favourable under future climate conditions, and aims to find genetic markers that can be used for faster breeding of wheat cultivars suited to future climates.

Supervisor: Kuno Kasak

Restoring degraded peat soils is an attractive, but largely untested climate change mitigation approach. Drained peat soils used for agriculture or for peat extraction are often large greenhouse gas sources. Restoring subsided peat soils to managed, impounded wetlands can turn these sources into carbon sinks. However, at present, the amount of scientific information available to guide such restoration decisions and assess the impact of these actions is still sparse and restoration outcome can be low carbon uptake and high methane emissions. Therefore, the overarching objective of this study is to provide an experimental and theoretical understanding how to restore wetlands with minimized methane and nitrous oxide emissions and maximized carbon uptake. The current study focuses on the spatial heterogeneity of methane, nitrous oxide, and carbon dioxide emission from restored wetlands using micrometeorological and field-based techniques. The research will be carried out in three restored wetland ecosystems in Estonia but with strong collaboration with international partners in Europe, Korea, and USA.

Supervisors: Margit Kõiv-Vainik, Kuno Kasak

Intensive forest management and agricultural activities cause diffuse pollution, which is harmful to freshwater ecosystems. Nature-based solutions for water conservation and pollution mitigation, such as sedimentation ponds and treatment wetlands, are effective solutions for cleaning polluted water from various sources. However, there is still too little scientific information to choose the optimal measures, especially in cold climate conditions. For an effective water treatment, it is necessary to develop sitespecific treatment methods by enhancement of existing technological solutions and by conducting indepth process-based research to identify different treatment processes and other factors influencing their performance. The PhD research will be conducted on full-scale treatment systems built to remove diffuse pollution from peatland forests and agricultural fields.

Supervisors: Elin Org PhD, Oliver Aasmets PhD, Vallo Tillmann MD, PhD

The human gut microbiome is a complex and metabolically active community that has a major impact on human health. Since the composition of the microbiome changes throughout life and is influenced by many different factors (eg age, diet, medication, health conditions etc), it is important to monitor changes in the microbiome over times. Sampling of the microbiome at multiple time points provides an opportunity to analyze the temporal variability of the microbiome and to evaluate changes in the microbiome along with changes in health parameters. As the majority of the studies so far have been conducted using a single measurement of the microbiome, the longitudinal data can provide additional information for microbiomeassociation studies, estimating personalized disease risks and directing personalized drug usage. In this doctoral project, we use gut microbiome data collected from several time points and evaluate the impact of the microbiome dynamics of both children (from birth to 9 years of age) and adults (Estonian microbiome cohort) on the development of disease risks.

Supervisors: Triin Laisk, Lili Milani, Reedik Mägi

500 million women use hormonal contraception (HC), but we lack the tools to select the most suitable contraceptive for each woman. This means millions experience unwanted side effects that range from relatively mild to potentially life-threatenung. Although we know that individual genetic variation affects drug metabolism and risk of side effects, we still lack relevant studies for HC. This PhD project will take advantage of the rich data available at the Estonian Biobank to describe the patterns of HC use and potential side effects. In the next stage, the potential association between genetic factors and side effects will be evaluated. Finally, we will analyse how the genetic risk of specific side effects modifies the overall risk in HC users. As a result, we will have a comprehensive overview of HC side effects, their temporal patterns, and the genetic risk factors, which can be used as an input for personalised medicine to select the most optimaal HC for each woman.

Supervisors: Anders Eriksson, Liisa Loog

Recent evolutionary studies have illustrated that human evolution is largely characterized by a series of long-range expansions, followed by contacts between groups of different ancestries leading to local ancestry mosaics that, in turn, provide potent fuel for natural selection in response to cultural and environmental pressures. While recent advances have allowed using ancestry patterns across the genome as well as ancient DNA (aDNA) data to shed light on recent adaptive evolution in Europe, fuelled by past population contacts, such processes are poorly understood elsewhere. This PhD thesis will take advantage of aDNA and whole genome sequence (WGS) data from contemporary populations to disentangle the dynamics of selection and gene flow in understudied regions of sub-Saharan Africa (>1400 genomes from 143 Bantu-speaking populations together with African aDNA data) and North Eurasia (>3,500 combined whole genome sequences from the Estonian and Korean biobanks). The adaptive evolution in these regions is of special interest due to the extreme cultural and climatic pressures (e.g., diet, extreme temperature, insolation, pathogen load) people of these regions have been subject to. The thesis will also make important methodological contributions to better accommodate low quality (aDNA) data and combine aDNA with large WGS datasets in North Eurasia for inference of evolutionary Dynamics.

Supervisor: Kelli Lehto

Large number of symptoms across different psychiatric diagnostic categories overlap, indicating a high degree of non-specificity among the symptoms and complicating clinical decision-making for clinicians. Cognitive symptoms, such as problems with memory, attention and concentration, form one such example. Cognitive problems are frequently present in very different psychiatric disorders, such as depression, attention-deficit/hyperactivity disorder, post-traumatic stress disorders and dementia, which are all disorders considered to have different mechanisms and treatment strategies. However, currently it is not known whether cognitive symptoms seen in patients with different psychiatric diagnoses are associated with similar or distinct mechanisms. This doctoral project aims to utilize the genetic, metabolomic, electronic health record and questionnaire data in the Estonian Biobank (N=200 000) to disentangle the genetic and epidemiological mechanisms underlying cognitive problems. The new knowledge from this PhD project would significantly advance our current knowledge on the genetic background of cognitive problems and potential medical predictors (e.g. diagnoses, biomarkers). Better understanding and specific prediction models for cognitive problems may aid clinicians in the future in making more accurate diagnostic and treatment decisions as well as plan timely prevention of cognitive problems, based on individual-level genetic and biomarker data.

Supervisors: Tõnu Esko, Erik Abner

Estonian biobank (EstBB) was set up as a population-based biobank, with the intention to spearhead the implementation of personalized medicine in Europe. To date, over 210 000 consented participants from all demographic distributions within Estonia (around 20% of the whole adult population) have voluntarily provided biological materials to EstBB. All the blood samples from the biobank participants have undergone DNA microarray-based genotyping and are periodically linked to national electronic health records. The genetic data of the biobank participants has been imputed utilizing a local genomic reference panel, which allows for a considerably higher imputation quality than with other non-population specific reference panels. As such, reliable genetic data on thousands of missense, stop-gained, loss-of-function and other functionally relevant variants has recently become available. However, a large proportion of these rare genetic variants have only been poorly described. The electronic health records per biobank participant generally span for over a decade, allowing our researchers to statistically associate these variants to common health traits and disorders. For example, an initial analysis of these rare variants has allowed us to identify a rare genetic variant, which is significantly associated with obesity and might affect the life quality of 1-in-115 Estonians. The aim of this thesis is to run a broad-scale analysis on the rare genetic variants present among the Estonian population, with the objective to characterize their effect on common complex diseases. The results from the broad-scale analysis of rare variants will allow us to consider additional in-detail studies of relevant functional genetic variants. Furthermore, this thesis will also concentrate on thoroughly analyzing the biological and clinical aspects of the previously mentioned obesity causing variant. Utilizing the already existing genetic and phenotypic data from Estonian biobank, this thesis will further our understandings of the molecular etiologies of complex diseases and assist in the implementation of personalized medicine in Estonia.

Supervisors: Alena Kushniarevich (Institute of Genomics, UT (UTIG), Kristiina Tambets, Georgi Hudjashov

In this project we will address two problems. First one is technical – the amount of genomic information that can be retrieved from ancient human genomes is usually low. Sparse genetic data restricts population genomic analyses to the ones that use allele frequencies, while analyses of longer genomic segments are not possible. To overcome this, we will use genotype imputation (GI) (inferring missing genotypes based on the available information in the genome sequence and a reference panel) to increase the amount of genomic data for downstream analyses. To optimize the GI performance in ancient samples of East European origin we will enrich the reference panel by compiling several high coverage human genome datasets including those from Estonia and Poland. Second, we will use both haplotype and frequency-based approaches to study population relatedness, mobility and social structure of the currently understudied Iron Age-Medieval period in Eastern Baltics. The outcome of the project will contribute to the technical improvement of ancient DNA analyses as well as to our understanding of the role of the Iron Age-Medieval period in the formation of today’s genetic and cultural landscape of Eastern Baltics.

Supervisors: Reedik Mägi, Triin Laisk, Kristi Läll

Complex traits, which have both genetic and nongenetic component, such as diabetes, represent a considerable burden to the healthcare system and quality of life. These conditions arise from a complex interaction between individual genetic variation, environmental and lifestyle factors. The summary statistics obtained from genome-wide association studies can be used to create polygenic risk scores (PRS) that summarize the effect of many genetic variants on a trait or disease and can be used to stratify individuals into different risk groups for risk prediction. In this PhD project, we plan to develop and implement a novel methodology for PRS calculation based on meta-regression transethnic meta-analysis framework to calculate scores for people with different ethnic backgrounds. We plan to to use this methodology in various international consortia that work with people of non-European or mixed ethnic backgrounds and to use UK and Estonian Biobanks data for model validation.

Supervisors: Ene Reimann, Elin Org, Reedik Mägi, Triin Laisk

Rheumatic Diseases (RDs) affect more than 40% of Europe's population and cause significant disability, pain, and reduced lifespan. Dysbiosis of intestinal microbiota i.e. deleterious alterations of the composition and/or function of the gut microbiota has been implicated in the pathogenesis of a number of diseases. However, the mechanisms through which intestinal dysbiosis contributes to disease pathogenesis are largely unknown. In this project, we aim to explore the relationship between gut microbiota, intestinal permeability, and SE. Further we aim to understand their role as drivers of disease onset and disease activity in osteoarthritis (OA), rheumatoid arthritis (RA), and spondylarthritis (SpA), as well as targets of preventive and therapeutic approaches. We will study the events leading to disease onset by taking advantage of Estonian Biobank data with available blood and feces samples. We will use a standardized GWAS analysis pipeline to evaluate the associations between genetic variation and defined outcomes of interest (SE, OA, RA, SpA) to identify potential genetic predictors. Similarly, we will analyze associations with metabolite profiles using NMR metabolites available in Estonian Biobank. This data together with ohter available omics datasets will be used to construct risk prediction models for studied RD diseases. The constructed risk prediction models for SE, OA, RA and SpA will be used to provide feedback to EstBB participants via the EstBB Participant Portal (currently under development). The same portal can be used to then collect feedback from participants.

Supervisors: Age Poom, Anto Aasa

Transport sector has witnessed faster greenhouse gas emission growth than any other economic sector over the past half century, and after a temporary decline during the COVID-19 pandemic, emissions have continued to increase. GHG emission and other environmental impacts of human mobility are often related to car-oriented transport planning, urban sprawl, and under-performing public transit. To reach climate and sustainable development goals in transport sector, a drastic change in current spatial planning, land use patterns, transport systems and technologies, and human behaviour is needed. This PhD project aims to provide a forward-looking big data-based approach for sustainable mobility planning in functional urban regions. Unprecedented availability of mobile big data enables us to receive an understanding of mobility behaviour and modal split in high spatial resolution, while also demonstrating the levels (and lack) of access to sustainable mobility options. Advances in computational technologies allow us to integrate population, mobility, and contextual data sets and forecasts to model sustainable transport networks for near and further future. These tasks will be executed in this project with using Tartu functional urban region as a case study area. The project will (i) provide an understanding of current mobility demand and mobility patterns in Tartu functional urban region (peri-urban, suburban, semi-central, and central areas) by integrating multiple mobile big data sets, (ii) develop a spatially optimised network of sustainable transport infrastructure with public transit and mobility hubs with first/last mile solutions in focus, and (iii) provide a spatial solution for transport infrastructure development that would consider population and climate prognosis and comply with sustainable mobility goals in 2040. The project will develop methods to replicate or reproduce similar approach in other functional urban regions.

Supervisors: Mikk Espenberg, Ülo Mander

Wetlands have long been managed (drained, conversion of land use) for human use, thereby strongly affecting greenhouse gas fluxes, flood control, nutrient cycling and biodiversity. Especially, wetland forests control terrestrial greenhouse gas (CO₂, CH₄ and N₂O) fluxes. The greenhouse gas (GHG) budgets of wetland forests are not known enough, especially in the tropics. Wetland forests are a global sink of atmospheric CO₂ and a crucial global stock of land organic carbon (C) and organic nitrogen (N). The balance between the GHG fluxes in wetland forests remains largely unpredictable, including the effects of abiotic environmental factors (e.g., soil water content (SWC), and temperature and biotic factors (e.g., plant community and microbiome parameters). Tropical regions create most of the global land-use based GHG fluxes, and the climate-change induced events further boost the fluxes. Mitigating GHG emissions is a challenging task for the 21st century, where wetland forests provide a powerful opportunity.

Identifying individual processes behind the GHG fluxes is still a major challenge. The role of microbiome in GHG budgets is unknown and must be integrated with the knowledge of the processes. The part of canopy and tree stems in GHG budgets is still overlooked, where there seem the microbes again have the role. This PhD project will be pioneering to synthesise fluxes from various spatial levels linking them to microbial processes. This will help to understand GHG fluxes and related environmental factors (e.g., microbiome) and create strategies for mitigation of the GHG emissions for managed wetland forests.

Supervisors: Ain Kull and Kristina Sohar

Dendrochronological methods have been previously widely used to study climate changes but in lesser extent also to assess changes in water level in both peatlands and on mineral soils in Estonia and worldwide. Since the tree-ring width depends on the environmental conditions it is best to use trees growing close to their ecological margin since any effect on tree-ring growth is stronger there.

The doctoral project will advance our knowledge of climatic and human impacts on peatlands, improve the dendroecological method and explore potential of trees as ecological indicators for peatland restoration.

The three main focus points of the doctoral study are the following:

(1) to use tree-ring width series to establish spatial and temporal impacts of drainage on tree growth along hydrosequence in the peatlands;

(2) to advance dendroecological methods for monitoring effectiveness of peatland restoration and afforestation based on tree rings as integral indicator of the ecological status of the peatland;

(3) to assess the climate change and historic weather events based on tree radial growth along soil hydrosequence and by clustering response in hydrologically contrasting sites.

The doctoral project will use partly pre-collected unstudied core samples and discs from well-monitored permanent plots in Estonian peatlands. Nevertheless, additional material will be collected during the project. Radial increment data will be studied in relation with habitat, soil, hydrological and long term climatological data.

Supervisors: Kadri Leetmaa, Kairi Kreegipuu, Bianka Plüschke‐Altof 

Our broader mission is to pioneer connecting research in ‘smartification’ and exclusion. We focus on ’smart rurality’ and the elderly typically disadvantaged spaces and social group in terms of the newest smart solutions. We will study the individual- and place-level enablers and barriers to smartification and carefully unpack the digital innovation biographies of four model localities in rural Estonia. We will apply the bottom-up strategy to explore the needs of smartification in double-excluded (rural, aging) localities in order to co-create the adjusted smartification process. The specific learning experiment (intervention) will be prepared and carried out to create smart data in the data-poor environment (rural localities) for the typically data-poor social group (elderly). The community researchers’ approach will be applied to facilitate the learning experiment. The participative qualitative methods will be combined with quantitative research design. This is followed by the systematic observations of the social innovation process. The candidate for the PhD-project is expected to actively participate in the planned fieldworks in close collaboration of the -years research project “Rethinking smartification from the margins: Co-creating Smart Rurality with and for an Aging Population” (PI K.Leetmaa, funded by Estonian Research Council).

Supervisor: Evelyn Uuemaa

Growing human population needs to increase food production without more negative impact on the environment. This situation has created a need for “sustainable intensification”: increasing agricultural yields while simultaneously decreasing environmental impacts. Nature-based solutions (NbS) such as riparian buffer strips and wetlands have been proven to be an effective measure at retaining sediments and nutrients leached from upslope agricultural areas, and in increasing carbon storage. However, in agricultural regions, riparian buffer zones and wetlands have been rather reduced in extent, or removed entirely to maximize arable cropland. To restore or create wetlands and riparian buffer strips is costly and therefore the potential restoration/creation sites must be selected carefully by considering site-specific conditions. Traditionally, such large-scale planning efforts are accompanied by extensive fieldwork, which reduces its applicability on larger scale. The aim of this PhD project is to identify the feasible and potentially most effective areas for wetlands and riparian buffer strips to reduce the nutrient runoff and increase carbon storage at regional (European) and national (Estonian) scales using remotely sensed data and novel spatial analysis methods.

Supervisors: Alisa Krasnova, Kaido Soosaar, Ülo Mander

Forest ecosystems play an important role in carbon (C) and water cycles by acting as C sinks and as sources of water vapor via evapotranspiration. In recent decades, climate extremes have increased and significantly changed forest ecosystems' C cycle and water regime. For instance, a severe heatwave in the summer of 2018 switched some Estonian forests from net C sink during the vegetation season of 2017 to net C source in 2018. However, only a few studies compare several forest ecosystem types within temperate and boreal zones regarding their longterm dynamics of C and water fluxes and characterizing their differences in tolerance to climate extremes. This PhD project will analyze temporal and spatial dynamics of C and water fluxes (evapotranspiration) and water use efficiency (WUE) in temperate and boreal forest ecosystems. Long-term datasets from several Estonian stations, FLUXNET, and ICOS (Integrated Carbon Observation System) stations (regional to global networks of eddy covariance flux measurements) in Scandinavia will be used for the analysis. The proposed thesis will link the dynamics of C fluxes with water exchange in various forest ecosystems of temperate and boreal zones. Specific research aims are: (1) to analyze the dynamics of C and water fluxes, and water use efficiency of various temperate and boreal forests, (2) to clarify the impact of climate extremes on these dynamics.

Supervisors: Martin Liira, Kalle Kirsimäe, Aivo Lepland

Shallow-water continental margin ferromanganese (Fe-Mn) concretions have been reported worldwide and in the Baltic Sea. The Baltic Sea, especially the eastern part of the Gulf of Finland, is known as one of the world’s unique areas where shallow-water Fe-Mn concretions abundantly occur. The process of forming Fe-Mn concretions and their distribution in the Baltic Sea is not well comprehended. It is thought to be a complex interplay between various factors, such as diagenetic processes, the discharge of groundwater, and the involvement of microorganisms that affect the supply of reactants and redox conditions. Nonetheless, it is uncertain which of these processes are more important and which specific factors are responsible for the precipitation of Fe-Mn and the integration of valuable trace elements.

Supervisors: Leho Ainsaar, Tõnu Meidla

Deep-time climate reconstructions help to understand the functioning of the Earth System and provide insights into the effects of climate changes on the biosphere. This project explores how the Earth transferred from extreme greenhouse climate to icehouse conditions during the Ordovician (443-485 My ago) and how this influenced the carbonate sedimentary environments in the Baltoscandian Basin. The PhD project focuses on sedimentary environment changes and facies dynamics during the episodes of disturbation of the carbon cycle, documented by the stable isotope studies (e.g., end-Ordovician Hirnantian and Silurian Lau events). These climatic events resulted in rapid sea level changes. Studies of facies dynamics during these events requires updated chemostratigraphic and biostratigraphic correlations. The environmental variations across the facies profiles from nearshore to deeper shelf in the carbonate basin can be analysed using the mineralised hardgrounds with special attention to phosphatic impregnation of hardgrounds. The Rare Earth Elements distribution in early diagenetic phosphates allows to restore the hydrodynamic conditions and distribution of oxic to suboxic zones during the critical Ordovician time intervals.

Supervisors: Alar Rosentau and Tiit Hang

The changing climate, rising sea level, increasing storminess, and possibility for tipping points leading to cascading, dangerous scenarios, call for better understanding of the functioning of the Earth System in the past, present and future. While meteorological records are short, the tideless and uplifting coast in the central Eastern Baltic Sea is unique because the elevated coastal formations and sediments serve as archives for deciphering past sea-level change and climatic events. The proposed PhD project aims to reconstruct the Holocene (last 11700 years) changes in the relative sea-level and storminess in western Estonia, based on analyses of the prograding coastal landforms and sediments. During the PhD project, new geomorphological and sedimentological evidences will be collected and analyzed from the selected coastal sections from western Estonia (including West Estonian Archipelago) to reconstruct palaeo sea-level and storminess.

Supervisor: Tarmo Tamm

Cellulose is one of the most abundant and sustainable natural resources on the planet, making it attractive for a wide range of applications, from paper and packaging to textiles and biofuels. However, the traditional production and disposal of cellulose-based products can have a significant environmental impact, contributing to global ecological challenges. The current project aims to develop new technologies that can reduce the environmental impact of cellulose-based products and improve their sustainability. In particular, a technology for turning cellulose from cotton-rich textile waste into foams for packaging or cushioning will be developed, employing “green chemicals” and ensuring maximum recyclability. The technology is intended to be swappable into today’s expanded polystyrene (EPS) manufacturing, ensuring a true impact. While paper recycling in well-established, the traditional wet approach does not meet modern technological and environmental requirements. Instead, by focusing on well-defined waste paper sources, the use of chemicals can be drastically minimized. Moreover, the consumption of water and energy will be minimized by studying possible bio-based dry binders and thermomechanical treatments. This research can open up new economic opportunities by creating new supporting a circular economy that prioritizes resource conservation and efficiency.

Supervisors: A. Pištšev, A. Vargunin,  S. Zh. Karazhanov

The PhD project is dealing with the functional properties of A15 intermetallic compounds, conductive oxide and hydride systems. The general idea is based on the main concept of crystal solid state chemistry that the physical properties of a material can be modified and controlled by changing the chemical bonds between different ions/atoms in the crystal structure, for instance, through charge transfer between metal and ligand, through the strength of interaction between ligand and metal, etc. The main goal of the study consists of two parts: the first one is to understand what modifications of the composition and structure could lead to an increase in the superconducting transition temperature in compounds such as A15. The second part of the research work is to establish rigorous parallels between the superconductivity mechanisms in A15 compound (e.g., Nb3Sn) and a number of conducting oxide and hydride systems (e.g., not fully oxidised stable forms of some binary oxides and oxyhydrides). Our theoretical assumption, based on a comparison of the crystal-chemical features of these materials, as well as on the combination of various experimental and theoretical data, is that the nature of superconductivity in them may be conceptually the same. This provides a good opportunity to study the mechanism of superconductivity in all these systems within the framework of a unitary approach of theoretical modelling. The results obtained can be applied to the development of modified or new superconducting materials that meet the requirements of electrodynamics of technical superconductivity. The research outcomes may have large potential for new-generation superconducting coils and wires operating at temperatures close to the boiling point of hydrogen. The project is interdisciplinary, bridging solid-state theory and experimental work in materials physics, on the one hand, and applied aspects of superconducting power engineering using liquid hydrogen as a refrigerant, on the other.

Supervisors: Raivo Jaaniso and Margus Kodu

The project aims to design novel structures from few-atom thick 2D materials with enhanced gas sensing properties such as high selectivity to specific gases and long-term stability. The heterostructures are based on graphene as an ultimately sensitive electrical transducer and the insulating or semiconducting material on top of it, acting as the gas receptor and protecting layer. The structures will be fabricated by laser-induced forward transfer and by assembling stacks with other transfer methods of 2D materials.

Supervisor: Jüri Lember

Main objects of research are pairwise and triplet Markov models (PMM and TMM). These models are generalizations of hidden Markov models (HMM). The research focuses on several directions: segmentation/decoding/denoising, parameter estimation in frequentist as well as in Bayesian setting, iterative algorithms.

Segmentation problem is estimating/prognosing the underlying hidden path given the observations. For HMM’s, a standard method is so-called MAP path obtained by celebrated Viterbi algorithm. For PMM’s Viterbi algorithm works just fine, but for TMM’s it cannot be applied not any more. Hence the need for alternatives. One goal of the theses is to find alternatives to Viterbi algorithm, certain iterative algorithms are under primary consideration. Those iterative algorithms have roots in Bayesian analysis, hence the link to the segmentation in Bayesian setting. It should be noted that in Bayesian setup no classical HMM-algorithm cannot be applied any more, hence the need for conceptually new approach. Besides time-consuming Monte Carlo methods, the above-mentioned iterative algorithms might be used. One of them – so called MM algorithm – outputs also parameters, so it can be considered as a segmentation as well as parameter estimation algorithm (in the HMM literature it is known as Viterbi training). Although notoriously bad in parameter estimation, the simulations conducted so far suggest that it might work well in segmentation. The link between Viterbi training and segmentation in Bayesian setup is innovative, unknown and needs theoretical study. Yet another objective of the PhD program. In parameter estimation, it is conjectured that Viterbi training might be adjusted so that the asymptotic bias would be reduced, but computational cheapness (in comparison with EM and other standard tools) prevails. The work in this direction is another objective of the current program.

Supervisors: Johann Langemets, Miguel Martin

In 2010, the concept of slicely countably determined Banach spaces was introduced to generalize separable Banach spaces which are Asplund or have the Radon–Nikodym property. However, several problems involving slicely countably determined Banach spaces have been unsolved since then.

The proposed doctoral project aims primarily to advance our knowledge on slicely countably determined Banach spaces by extending this notion to nonseparable spaces. The secondary goal is to characterize slicely countably determined sets in Lipschitz-free spaces and their duals. Geometry of Lipschitz-free spaces has become a very active research subject. Every Lipschitz function between metric spaces admits a canonical linear extension between the corresponding Lipschitz-free spaces. This fundamental property makes Lipschitz-free spaces an efficient tool to study Lipschitz functions, which appear in many contexts.

Supervisor: Tiina Kraav, Evely Kirsiaed

The importance of technology in today's education landscape cannot be overstated. Computer-based testing and assessment make sense in terms of conserving various resources. At the upper secondary level, an e-exam is being commissioned by the Ministry of Education and Research, which ought to be fully computer-assessable. Among our neighbouring countries, a computer-based national mathematics examination is being held in Finland and Norway, and preparations are being made in Sweden. But none of these exams are fully computer-assessed. Mathematical literacy, as measured by the PISA test, is also only partially computer-assessable (Bardini, 2015). 

Educational researchers have been interested in the comparability of paper-based and computer-based assessment tools for a while, i.e. whether the tests under comparison measure the same thing. Different research yields different results (Bohak, 2021), suggesting the need for further studies. Researchers also reveal different factors that may influence the results of mathematics tests. Bohak (2021), for example, showed that computer familiarity is not a predictor of test scores.   

Existing centralized tests already in use in Estonia have important shortcomings. They are used repeatedly without modification, do not include programmable randomization, and are often multiple-choice tests which allow very limited feedback. Therefore, it is necessary to clarify whether and to what extent computer-based tests are also computer-assessable, especially with regard to problem solving, which is the focus of mathematics in the new national curriculum. Measuring problem solving skills by computer is of great interest in today's educational research landscape, and it is natural that it will be both substantively and technically feasible to the satisfaction of all parties in the near future. 

The planned research aims to answer the following questions: What are the mathematical knowledge and skills that need to be tested in the first place if the result of the exam is a means to enter higher education? What are the possibilities for computer-assessable testing of mathematical knowledge and skills?

The practical benefit of the thesis is a collection of computer-based tests for practicing mathematical problem solving, also a 'mock exam', which is fully automated and accessible to upper secondary school students at any time to assess their current knowledge/skills. More broadly, it is hoped that the thesis will have an impact on the development of a science-based national curriculum in mathematics.

Supervisors: Mariliis Klaas, Kristina Mäemets-Allas

Fibrotic diseases are widespread and difficult to treat diseases in the world. Dupuytren's disease is a common chronic palmar fibrotic disease in which the subcutaneous tissue thickens, causing permanent flexion deformities. Since the typical onset of Dupuytren's disease is between 40 and 59 years of age and the incidence increases with age (up to 29% of people in the 75+ age group), Dupuytren's disease is a significant burden on the health care system in the context of an aging population. So far, there are no modern treatments for this disease. The aim of this project is to characterize changes in the extracellular matrix of Dupuytren's disease affected tissue and to investigate which molecular mechanisms are behind the onset and progression of the disease. We aim to characterize which components of the extracellular matrix are important in disease mechanisms and how it influences the macrophages and fibroblasts accumulated in the damaged tissue to promote inflammation. Using modern high-throughput technologies in combination with functional experiments in tissue culture, we will describe the molecular mechanisms important in the mechanisms of fibrosis. The knowledge gained from the research project will allow the discovery of novel drug targets that can be used to develop treatments for Dupuytren's disease as well as other fibrotic diseases.

Supervisor: Tiina Tamm

All viruses, regardless of genome type and size, utilize the host cell’s translation machinery, specifically ribosomes and translation factors, for the synthesis of viral proteins. Certain viruses employ specialised RNA elements, called internal ribosome entry sites (IRESs) to recruit ribosomes directly to their RNA. The use of such internal initiation allows specifically decrease the cellular protein synthesis and increase the translation of viral proteins.

The Cricket Paralysis Virus intergenic region (IGR) IRES facilitates translation of viral structural proteins. This project uses genetic, biochemical and structural approaches to clarify how the IGR IRES-mediated translation initiation is regulated during virus infection. The project focuses how viral protease alters the functionality of cellular translation machinery. The project results in a deeper understanding of virus-host interactions, which in turn allows the development of therapeutic strategies to effectively control the viral diseases.

Supervisors: Marje Kasari, Villu Kasari

Recombinant proteins are an important group of products that are widely used in various sectors, including food and pharmaceutical industries, in production of textiles, detergents, cosmetics and biofuels. Proteins can only be produced by living cells, and bacteria are often used for this purpose. Unfortunately, antibiotics must be used in bacterial production process. Their complete elimination from the final product and the disposal of production waste are expensive and difficult but required by regulation. The large-scale use of antibiotics increases the risk of the spread of antibiotics resistance. The aim of this doctoral project is the development of an E. coli-based biotechnological platform, which enables the high-yield production of proteins without the use of antibiotics. Our solution is equal to the existing ones in terms of production efficiency, but since there is no need to use antibiotic selection, the user does not need to prove that the final product is free of antibiotic residues. This is very important especially for enzymes used in the food industry and for pharmaceutical proteins. Also, the created platform significantly reduces costs and risks in waste management.

Supervisor: Taavi Lehto

The recent success in the mRNA-based therapeutics has shown that this class of therapeutics is ready for wider medicinal application. As the mRNAs have very poor bioavailability when administered alone, they require drug delivery systems (DDS), such as lipids, peptides or polymers, to package the mRNA into nanoparticles (NPs) In turn, NP formulation increases its stability and improves delivery/bioavailability to allow for therapeutic activity of the mRNA at the target tissue/organs. In this study we will use a recently developed highly efficient biopolymer/peptide-based DDS for modified mRNAs to develop a scalable nanoparticle synthesis technology for the production of next generation size-defined biopolymer/mRNA nanoparticles. Since with the conventional formulation technologies it is difficult to control the stability of the formulation at higher concentrations of materials used for formulation production for pre-clinical and clinical grade nanoparticles, we will use a scale-independent microfluidic formulation technology in combination with downstream purification and sterile processing to achieve this goal. Together with validating these novel biopolymer/mRNA nanoparticles in cell cultures and mouse models for mRNA delivery efficiency, these findings will provide state-of-the-art knowhow on how to scale-up the production of biopolymer/mRNA nanoformulations for future preclinical and clinical studies.

Supervisors: Mart Loog, Ilona Faustova

Most cellular processes are regulated by protein phosphorylation. About 2% of eukaryotic genes code protein kinases, which phosphorylate at least 30% of the proteome. Although many kinasesubstrate pairs have been identified, in most cases it is not known how kinase specificity is achieved, to what extent and under what conditions a substrate is phosphorylated. The main goal of this doctoral thesis is to describe the specificity determinants of kinases Pho85, Mps1 and Cdc7. These are highly conserved kinases that control critical cell cycle processes. These kinases have been thoroughly studied by genetic methods, but we are lacking mechanistic understanding of their function. The Loog lab has extensive experience in cell cycle and phosphorylation studies, and this project plans to apply this to unravel the signalling mediated by these three kinases. Pho85p, Mps1 and Cdc7 are highly conserved, therefore the knowledge from yeast can be used to study these kinases in human cells. These kinases are central regulators of cell cycle processes and changes in their activity is often correlated with cancer. So far, kinase inhibitors for cancer treatment have been mainly designed to bind to the active site of the kinase. However, since kinase active sites are homologous and conserved, these inhibitors often have off-target effects. As kinase specificity seems to be mainly governed by distal docking interactions, the knowledge gained from these specificity studies can be used to design specific inhibitors to these central cell cycle kinases.

Supervisors: Professor Andres Merits

In their hosts, different alphaviruses cause encephalitis or fever, rash and arthritic symptoms. In infected cells, these viruses form membrane-bound replicase complexes (RCs) consisting from viral nonstructural proteins (nsPs), RNA and host proteins. This project aims to study the molecular basis of compatibility of RC components using trans-replicase system and/or virus genomes. Analysis of determinants responsible for alphavirus RNA template recognition and gene expression will be performed by analysis of the physical interactions of replicase proteins with the 5’ ends of the genomes. We will also analyze the impact of codon-usage in structural region on alphavirus replication and pathogenicity. Finally, we hypothesize that G3BP proteins (Rasputin in mosquitoes) are involved in recruitment of viral RNA during RC formation. The impact of G3BPs, Rasputin and their mutants on this process will be analyzed. In addition to these directions PhD student will take part in other ongoing studies, including studies dedicated to role of capsid protein in virion formation and characterization of compounds targeting replicase proteins of alphaviruses.

Supervisors: Reet Kurg, Siret Tahk

The ability to survive an immune attack has been recognized as one of the major hallmarks of cancer progression. Tumour microenvironment (TME) favours hijacking the so-called immune checkpoints (IC) supporting tumour persistence and growth Monoclonal antibodies (mAbs) inhibiting IC signals and thereby enabling the activation of anti-cancer immunity have become one of the major advances in cancer therapy. However, only on a subset of patients respond to current IC therapies and novel immune cell activating approaches are needed. Multi-pass transmembrane proteins (MPTP), like certain G-protein coupled receptors (GPCRs) and transporters, are attractive cancer targets in addition to the established single pass IC-s, as several of MPTPs carry immunomodulatory functions within the TME. MPTPs have not yet been successfully targeted by mAbs due to difficulties in antigen production – they have a complex structure and depend on surrounding cell membrane lipid bilayer to maintain their native, active 3D-fold structure. Viruslike particles (VLPs) are hence great way to express MPTP antigens for immunization and screening of antibodies, using biologically more distant species chickens as the host. This PhD project focuses on developing novel mAbs targeting immunomodulatory multi-pass transmembrane proteins in the tumour microenvironment, which are involved in inhibiting antitumour immunity and promoting tumour growth. This would enable to enhance the efficiency of immune therapy and expand the cohort of treatment subjective patients.

Supervisors: Vijayakumar Anand and Aile Tamm

Computational imaging is one of the rapidly evolving areas of imaging. Most of the computational imaging methods transform the object information into a summation of shifted and scaled scalar optical fields. The resulting intensity distribution read by the image sensor is reconstructed into object information. This PhD project titled, “Computational imaging using vector optical beams generated from micro/nano optical devices,” aims to employ vector optical fields as the building block of the intensity distribution. Consequently, the summation is no longer a scalar summation but a vector one making it sensitive to changes in polarization of the object along 3D spatial and spectral dimensions. The existing computational reconstruction methods are only suitable for scalar optical fields. This PhD project will create new knowledge on vector convolution and correlation. The vector optical fields will be generated using novel engineered polarization sensitive micro/nano optical modulators. The modulators will be manufactured using advanced lithography procedures and implemented for experimental demonstration. Novel multidimensional compact, light-weight microscopes will be realized using the new vector correlation principles beyond the state-of-the-art.

Supervisors: Veiko Palge, Dirk Oliver Theis, Juhan Matthias Kahk

Quantum computation is a novel paradigm of computing which is capable of solving problems that cannot be tackled using conventional computing. In particular, quantum computers are able to efficiently simulate large quantum systems, a task which is beyond the reach of conventional computers irrespective of their size. This is of great interest for quantum chemistry and materials science, which underlie chemical and pharmaceutical industries and development of new materials and technologies. While fault tolerant quantum computing is predicted to arrive earliest in a decade, near term quantum devices which involve a few hundred qubits are becoming a reality presently. The project focuses on simulation of quantum systems that are of interest in quantum chemistry and solid state physics using the paradigm of variational quantum algorithms, as well as fault tolerant quantum computation. Analysis, benchmarking, testing and development of the range of methods that are used for simulation and computation of electronic structure and energy surfaces need to be carried out in order to make progress in quantum simulation on near term quantum devices.

Supervisor: Jörg Pieper

In the recent years, effects of climate change including drought and high-light conditions have become more abundant. Therefore, an adaptation to these changing environmental conditions will become unavoidable for a sustainable development of agriculture in the future. One promising strategy is to study native adaptation mechanisms in photosynthesis to battle environmental stress. In this regard, two promising bioprotectants are trehalose and glycerol, which act as stabilizer and plasticizer, respectively, for the photoactive proteins responsible for photosynthesis in plants.

Trehalose is a specific sugar found in organisms able to survive extreme external stresses, such as high or very low temperatures or periods of complete drought. Most importantly, trehalose was shown to preserve the function of native photosynthetic proteins upon dehydration. Glycerol is a highly hydrophilic compound that is able to attract water, thus, leading to an increase of the protein surface hydration. This plasticizing effect ensures the functionally important protein flexibility and prevents harmful aggregation or misfolding of proteins. However, the molecular mechanisms of the protective roles of trehalose and glycerol are still debated and often poorly understood.

Neutron scattering methods are well-suited for direct nanoscale investigations of protein structure and mobility under nearly native conditions as well as to study their interactions with bioprotectants. Small angle neutron scattering with contrast variation will be used to investigate the location of the trehalose or glycerol molecules with respect to the protein surface and the hydration shell to shed more light on the particular molecular interaction mechanisms. In addition to structural integrity, proteins also have to preserve a specific level of flexibility to perform their function. Quasielastic neutron scattering spectroscopy will be employed to directly study the protein flexibility under environmental stress conditions in the presence of bioprotectants.

Supervisor: Marco Kirm

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 create favourable conditions for appearance of intrinsic emissions, 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), consisting of results of theoretical studies of material properties, and experimental results available. Thereafter the selected materials will be synthesized as pure compounds and their solid solutions, their properties modelled and studied in 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 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 non-expensive low-dose scans for pediatric, prenatal and neonatal diagnostics in every major hospital, allowing early identification of tumour’s and other harmful conditions.

Supervisors: Mikhail G. Brik, Aleksandr Luštšik, Anatolijs Popovs

The doctoral project will be focused on the combined experimental and theoretical studies of a series of pure and purposefully doped oxygen-based spinel compounds AB₂O₄ which are technologically important functional materials for lighting, optical thermometers, detectors and dosimeters of radiation and even optical windows for projected fusion reactors. The PhD research allows to identify directions towards smart design of materials with desired optical and mechanical properties. Based on the results planned, we anticipate a number of the “structure-property” and “property-property” trends with strong descriptive and predictive power to be uncovered, which would make a noticeable contribution to both fundamental and applied research.

Supervisors: Miia Rannikmäe, Regina Soobard, Jari Lavonen

Society faces many multifaceted problems which arise through complex social, environmental and economic processes and are closely related. Those problems are such as poverty, food security, access to healthcare, climate change, etc. Resercah has shown that during the gymnasium years students` problem solving and decision-making skills progress little and there is the need to improve this.

The goal of current research is to determine teachers` readiness to interact with wicked problems in the classroom and develop and evaluate theoretically justified three-dimensional model for learning progression across school levels promoting coherent knowledge in use, core ideas in science and cross cutting concepts in the framework of (exemplary) wicked problems. 4 interactions with wicked problems will undertake over 1,5 years period by teachers’ teams. Students and teacher’s self-efficacy will be monitored and the effect from school to society will be determined by interviewing sample of school leavers (at tertiary conditions) with respect of their understandings of wicked problems.

Research data are collected among Estonian teachers and students` and for the researcher good command in Estonian language is obligatory.

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

Supervisor: Regina Soobard, Birgit Viru

Society needs to meet challenges and seek solutions in environmental, social and health issues. Science education in preparing students as active and responsible citizens needs to address such issues, enabling students to act, think and make decisions in considering relations between multiple disciplines. Special attention needs to focus on climate education. Since the beginning of the Industrial Revolution, the Earth´s average temperature has risen by 0.8°C. This has led to an increase of extreme weather conditions (floods, droughts, heat waves, strong storms, extreme precipitation) with the greatest effects on natural ecosystems, the economy, people's health, way of life. To mitigate against climate change and acting to slow down global warming, it is recognised as necessary to increase people's environmental awareness in particular through climate education. To make changes in the way human think about their role in climate change mitigation, it is important to review the effectiveness of how climate education is promoted in schools so as to promote a more informed generation, which is able to make science-based decisions in the future and can also change the habits and way of thinking of their family members. The goal of this research is to determine basic school students’ conceptualisation of climate, climate change, climate change mitigation and how social interactions (media, family, friends) are influencing a changing perception into behavioural change. Additionally, to develop an evidence-based Moodle course for targeting awareness and attitudes towards relevant aspects about climate and stimulating action by propagation useful solutions from GIS and to identify effective learning pathways for promoting climate education through outcomes derived from stages 1 and 2. Research data are collected among Estonian teachers and students and for the researcher good command in Estonian language is obligatory.

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: Riho Vendt, Viktor Vabson

The sustainability of humankind is more than ever linked to the health of our environment. Various techniques are used to investigate the state of the environment, while the best temporal and spatial coverage and reaction speed can be reached using remote sensing methods. Remote sensing instrumentation has shown great evolution in recent decades, allowing us to remotely sense the Earth's environment from various platforms, including space, aircraft, drones, boats, fixed platforms, and even underwater. Yet, the metrological support of remote sensing was initially overshadowed by technical and logistical extreme difficulties during instrument and network development. As a result, a vast amount of data has been collected without firm SI traceability, resulting in weak or missing uncertainty estimates. These data cannot be used as strong arguments when applying pressure on the financial and political rulers of the world to take necessary environmental actions. In order to solve the remote sensing uncertainty problem, dedicated institutions (ESA, NASA, NOAA, EUMETSAT, JRC etc.)) have initiated several projects over the past decades (CZCS, SeaWiFS, SeaBASS, MOBY, BOUSSOLE, SIMBIOS; MERIS, AERONET-OC, CEOS OCR-VC / INSITU-OCR activities, Copernicus OC-SVC, PACE). Numerous field inter-comparisons have showed that the agreement between the results is insufficient to meet the strict vicarious calibration criteria. Recent ESA/EUMETSAT projects (FRM4SOC Phase I and Phase II) were dedicated to the end-to-end uncertainty of ocean color (OC) measurements. In these projects, Tartu Observatory was responsible for instrument calibration and characterization. One of the main conclusions is that a fully characterized reference radiometer with superior optoelectronic parameters is needed in order to validate the commonly used field instruments and evaluate the final uncertainty of the contact measurements under all possible measurement conditions according to the generally accepted ISO GUM rules. Taking into account the observatory's role in the research, its long experience in designing of the remote sensing radiometers and the recent investments into the infrastructure, we've decided to help the remote sensing community with building the fully characterized portable reference spectroradiometer for the ocean color measurements.

Supervisors: Mihkel Pajusalu

As of our current knowledge life only exists on planet Earth and needs certain chemical and physical conditions to exist. Field of Astrobiology explores the limits of the possible conditions that could give rise to and sustain life; and possibility of finding life on other places in addition to Earth. Venus is the closest planet to Earth in terms of distance and most similar in size, but it hasn’t been explored in situ since the 1980's. While the temperature and pressure on the surface of Venus is too high for any kind of life to exist – because of the thick CO2 atmosphere and the greenhouse effect it produces – in the clouds of Venus the temperature and pressure are potentially suitable for life. There are many questions and unknowns about the chemistry and conditions on Venus. Among the essential aspects to know is the acidity of the clouds of Venus. It is essential to determine what kind of chemistry (including life) there could exist. The objective of this PhD project is to further develop the Tartu Observatory pH Sensor (TOPS) to measure the acidity of the clouds of Venus as part of Venus Life Finder mission and possibly work on developing an oxygen sensor for the same mission. In addition, as Venus is the most similar object in our Solar System to Earth, a better understanding of processes and history of Venus could help us better understand our own planet. This work will also further the goal of developing the field of Astrobiology in Estonia and Europe and will be done in close cooperation with international partners.

Supervisors: Anna Aret, Colin Folsom, Mihkel Kama

Accretion disks around young stars are the site of planet formation, and they regulate the last stages of star formation. Understanding accretion disks is therefore crucial for understanding planet formation, and understanding the interaction between stars and disks important for understanding both early stellar evolution and disk evolution. Hot pre-main sequence stars (Herbig Ae and Be stars) are particularly well suited to studying the impact of accretion on stars, since they have radiative outer layers and thus minimal mixing. Initial observational work, supported by theoretical modelling, has shown that accretion in these stars can modify stellar photospheric abundances to reflect inner disk abundances, provided a moderate accretion rate. Most clearly, this leads to a connection between photospheric abundances and gaps in disks. In these cases the gap leads to a depletion of refractory elements in the inner disk, which is reflected in photospheric chemical abundances. This project will use stellar spectroscopic observations and abundance analysis to further investigate this star-disk connection, with the goal of making useful predictions about disk chemistry, disk structure, and possibly even the presence of planets.

Supervisors: Mihkel Pajusalu, Martin Valgur

The proposed research project aims to develop a local mapping capability for mobile rovers using machine learning methods and camera data to generate spatial and semantic content about the surrounding environment, with the goal of enabling off-road and extra-terrestrial exploration, such as on the Moon or Mars. The project will investigate the applicability of machine learningbased spatial data generation methods, including occupancy networks, for offroad and extra-terrestrial environments using self-acquired and simulated data. The developed techniques will be first tested in simulated Lunar environments and off-road scenarios on Earth before being deployed on actual Lunar or planetary rovers, such as KuupKulgur.

Supervisors: María Benito, Rain Kipper

Since the global models of the Milky Way of the 1950s-1960s, most notably those of G. Kuzmin and J. Einasto, our understanding of the Galaxy has changed dramatically. Arguably one of the great breakthroughs in our understanding of the Galaxy came in the 1970s, when observational data were sufficient to suggest that galaxies are embedded in huge dark matter coronae or halos. Another milestone in our understanding comes through the European Gaia satellite, launched in 2013. Gaia has provided precise position and velocities for more than 33 million stars and has unravelled a set of out-of-equilibrium perturbations in the stellar disc of the Galaxy. The distribution of mass in the Milky Way is an important aspect that allows the current cosmological model to be tested. The above mentioned discoveries can tamper our modelling abilities of the Milky Way. For this reason, we must quantify the role of out-of-equilibrium forces acting on the total gravitational potential and their impact on the construction of global dynamical models of the Galaxy. To what extent do commonly adopted assumptions, such as axisymmetry or stationarity, bias the reconstruction of the Milky Way dark matter distribution? This PhD project aims to investigate these questions and to develop new modelling techniques, abandoning traditional assumptions, for a robust reconstruction of the dark matter distribution in our Galaxy.

Supervisors: Mihkel Kama, Anna Aret, Luca Fossati

In the study of extrasolar planets, some of the key questions concern how stochastic and deterministic planet formation processes combine to produce the structural and compositional diversity of planetary systems. More generally, questions about the potential for habitable worlds elsewhere are also of high scientific and public interest. In this project, the student will study planetary systems around early-type stars, specifically focussing on the evaporation of their atmospheres or surfaces and the transfer of mass from the planet to the star. Early-type stars are excellent laboratories because their radiative envelopes mix very inefficiently, which allows low levels of accretion – such as from an evaporating close-in planet – to dominate the composition of the stellar surface. The student will implement theoretical models in a computer code and use spectra from Tartu Observatory and other telescopes to constrain the properties of specific star-planet systems. The project will support the Tartu Observatory Stellar Physics Group’s work in the ESA Ariel space telescope mission and their leadership in the EXOHOST Twinning project.

Supervisors:  Jaak Nerut and Enn Lust

Considering the European Union's climate goals and energy security requirements, it is extremely important to increasingly use renewable energy. This means larger-scale construction of solar and wind energy parks in Estonia. Since the electric grid is not able to withstand all the green energy, it is possible to store excess energy as hydrogen. Hydrogen can be reused in electricity generation, used in the transport sector and in the chemical industry. One possibility is to use a hydrogen-air fuel cell in transport, namely the proton exchange membrane fuel cell (PEMFC). The slow oxygen reduction reaction (ORR) at the cathode is holding back further widespread adoption of PEMFC technology. Namely, catalysts based on platinum and platinum alloys are used as ORR catalysts, because they are active and time-stable materials. Since platinum group metals are a critical resource, this significantly increases the price of a PEMFC. One alternative could be the introduction of multicomponent high entropy alloys (HEAs) as ORR catalysts. The doctoral thesis uses an algorithm based on machine learning to predict the composition of ORR catalysts with suitable properties. In the doctoral thesis HEAs are synthesized using different methods. Synthesis optimization is carried out until a HEA with the correct phase composition is achieved. Physical characterization and electrochemical measurements of the catalysts will be carried out. The most promising catalysts for ORR activity and time stability are used in PEMFCs.

Juhendajad: Rasmus Palm, Enn Lust, Jaak Nerut, Angélica María Baena Moncada

Metal-organic frameworks (MOFs) include a wide variety of meso- and microporous structures with the potential to incorporate different metals and heteroatoms. Carbonisation of MOFs yields highly porous carbon materials with uniformly distributed catalytically active functional centres. The aim of this PhD project is to synthesize and carbonise MOFs yielding highly functional catalytically active model carbon materials. The MOF derived materials will be used to investigate the effect of different active sites in a carbon material to the adsorbate/adsorbent interactions important for energy storage and energy conversion characteristics. The focus of this PhD project will be to use such MOF derived carbon materials to investigate the beforementioned effect of active sites on adsorbate/adsorbent interactions crucial for energy storage and conversion applications. This includes the use of MOF derived carbons to investigate the effect of different active sites on hydrogen adsorption and hydrogen spillover effect, on the catalytic hydrogen storage properties of nanoconfined hydrides, and on the catalytic activity of oxygen reduction reaction kinetics at activated materials. The PhD project will involve the use of various experimental methods like gas adsorption, x-ray diffraction, Raman spectroscopy, and more. In addition, neutron scattering methods will be used to investigate the structures and processes of importance in/at the MOF derived carbons for energy storage and conversion applications.