Supervisor: Aveliina Helm
In order to address global change drivers and secure sustainable provision of ecosystem services, we need to rethink how we prioritize land for production and biodiversity protection or how we combine those two needs. Landscape-scale ecological restoration and sustainable planning of landscapes are of crucial importance here. We use Estonian production landscapes (mostly agricultural landscapes, including high-nature value agricultural areas) to study the biodiversity-production optimization and asses the current, historical and future condition of main ecosystem services. Doctoral project will address increasingly strong need for spatially explicit and evidence-based local solutions for landscapes (i.e. personalized land-use solutions).
Supervisors: Urmas Kõljalg and Kessy Abarenkov
There are probably more than 10 million species currently living on earth. The volume of their occurrence data is increasing rapidly. Global Biodiversity Information Facility (https://www.gbif.org) provides over 1.6 billion open data records on more than 2.5 million species. This is the world’s largest biodiversity open data portal. According to the eBiodiversity portal (https://elurikkus.ee/en) there are more than 4.6 million data records of more than 30 000 species found in Estonia. The number of open data records is growing fast globally as well as in Estonia. This is good news for the scientists and stakeholders who are looking for the data to evaluate climate change, loss of biodiversity, nature protection, invasive species, etc. because all these data are freely searchable and downloadable. However, there is a serious drawback, viz. lack of visualisation tools which hinder the rapid understanding of species occurrence data. Therefore, the aim of this PhD project is to develop new generation of the 3D visualisation tools for the species data records. Different sets of tools will be developed for the researchers, citizen scientists, schoolchildren, policy makers, etc.
This project will for the first time bring together species occurrence records based on specimens, observations, eDNA samples and literature in 3D visualisation. In addition we will implement Taxon Hypotheses (TH) paradigm recently developed by our research group (Kõljalg et al. 2020). The TH approach allows to manage and communicate species occurrence data on organism level. Species and organisms are communicated via Digital Object Identifiers (DOI). This innovative project will collaborate with other digital infrastructures like European Nucleotide Archive (ENA), Elixir Hub, GBIF, CERN, Pensoft, etc. under the H2020 project "Biodiversity Community Integrated Knowledge Library" (BiCIKL, 2021-2024). This will ensure the implementation of our novel 3D tools in these global e-infrastructures.
Supervisors: Mari Moora, John Davison, Martin Zobel
Many ecosystems exhibit alternative steady states, dominated either by woody or open vegetation. Perturbations, including herbivory and fire, have been proposed as determinants of vegetation structure, but it remains unclear to what extent microbial organisms contribute to the dynamics of such ecosystems. The goal of this proposal is to understand whether mutualistic microbes play roles as agents of perturbation and codrivers of ecosystem transition between alternative stable states. The project will combine observational and experimental approaches to study the diversity and guild structure of plants, fungi and bacteria in dynamic ecosystems, and determine whether particular mutualistic partners drive ecosystem transitions. Expected project outcomes will be fundamental knowledge about the mechanisms driving ecosystem succession, as well as practical information about manipulating microbial organisms for sustainable ecosystem management and restoration.
Supervisors: Meelis Pärtel, Maarja Öpik
It is well known that biodiversity is essential to guarantee ecosystem services, and the importance of microorganisms has been highlighted recently. Biodiversity is essential, but it is still a composite metric based on a large number of taxa. Therefore, we can understand ecosystems much more by knowing the rules of species distributions. During recent years the data on soil-inhabiting fungi has massively increased because DNA-based methods allow detection and identification of many more species. In addition, methods to model species distributions have advanced considerably, including Bayesian statistics and machine learning. Species distribution modeling is conceptually related to dark diversity methods, such as estimating habitat suitability from species co-occurrence data. In this project, we synergistically merge the strengths of these methodological aspects and include big data of soil organisms. As a result, we are well equipped to estimate how the distribution of soil fungi can contribute to ecosystem services. We explore mostly the ecosystem’s capacity to store carbon, but also other services, including mushroom picking and ecotourism. We will use both global databases but also data gathered by the host group. We also sample various Estonian ecosystems, such that data will be readily available to improve the planning of ecosystem conservation.
Supervisors: Sten Anslan, Leho Tedersoo
The main purpose of this project is to develop practical tools for bioinformatics analysis of long DNA reads derived from third-generation DNA sequencing platforms. It is critical to develop scripts for custom segmentation of long reads for efficient recognition of chimeric sequences and relatively conserved and unconserved regions for phylogenetic placement and species-level recognition, respectively. The collection of scripts and reference data sets will be integrated into the PipeCraft workflow to enable easy data analysis for non-bioinformaticians.
Supervisors: Olesya Dulya, Leho Tedersoo
The main purpose of this project is to determine the patterns of endemicity and biodiversity hotspots for soil fungi globally and on a European scale and to assess the uniqueness of areas with specific soil properties and vegetation in Estonia. The analysis is based on previously generated, high-quality PacBio high-throughput sequencing data combined with further refined methods and metrics used for determining endemicity. The outcomes of this project estimate the need for fungal conservation in Estonia, Europe and the Earth in general.
Supervisor: Kadri Runnel
Most of what we know about the state of biodiversity is based on small well-studied subsets of species. This PhD project focuses on the vast forest biodiversity that is hidden due to cryptic life style or belonging to poorly known taxon groups, e.g. fungi, bacteria and meiofauna. During the recent decades, the novel DNA meta-barcoding techniques have become a powerful tool for getting insights into such “inconspicuous” diversity, but the possibilities of this data-source remain nearly unused in conservation. The main reasons are difficulties in interpreting of the diversity patterns in DNA metabarcoding data (may partly represent non-viable species occurrences); upscaling such data to the scales of management relevance (forest stands, landscapes), and relating it to the commonly used landscape scale indicators. This PhD project evaluates how to translate the biodiversity patterns in DNA metabarcoding data into meaningful information for conservation of inconspicuous forest diversity. This will be assessed through three case-studies that aim to: (1) identify those assemblage characteristics in DNA-metabarcoding data that describe the state of inconspicuous forest biodiversity, (2) describe links between the state of inconspicuous biodiversity and landscape degradation, (3) set the biodiversity patterns in DNA metabarcoding data into the context of commonly used indicator species or structures in the forest.
Supervisors: Rasmus Palm, Enn Lust
The development of alternative hydrogen storage methods is an important component for the implementation of a sustainable “green” hydrogen-based economy. Hydrogen storage in hydrides is a potential safe alternative to contemporary hydrogen storage systems. Still, for their application in commercial solutions, the temperature of hydrogen release, the rate of hydrogen release, and cyclability must be improved.
This doctoral project encompasses the synthesis and hydrogen storage properties of complex materials and the determination of hydrides’ characteristics confined in mesoporous carbon scaffold materials. The doctoral project involves the development and optimization of the synthesis methods for the nanoconfinement of hydrides and catalyst-doped hydrides. Nanoconfinement is achieved through the economical melt-infiltration and ball-milling methods, and catalyst doping is achieved through the selective partial chlorination of the carbides during the synthesis of the used carbon scaffolds. The capability of the synthesized composites to store hydrogen is determined with temperature-programmed decomposition, X-ray diffraction, and in situ neutron scattering methods. In situ neutron scattering methods enable to determine the structural transitions occurring during decomposition and formation of the hydrides, thus analyzing the influence of nanoconfinement on the composites capability to store hydrogen. Improved understanding of the decomposition and formation process mechanisms and kinetics of nanoconfined hydrides obtained during this doctoral project makes it possible to design competitive hydrogen storage systems, which would be a considerable contribution towards an emission-free economy.
Carbon materials have a vital role in a multitude of applications of which energy storage and conversion systems are crucial for the development of a low-carbon economy. For the optimal performance under different application conditions, carbon materials with well-determined structures and compositions are essential. Thus, the ability to obtain carbon materials with a wide variety of properties in a controlled manner is of high interest. Thus far, post-pyrolysis changes and modifications to the carbon structure have been irreversible or have been achieved under very high pressures, which are not cheap and not applicable in commercial systems. Our group has recently (2021) demonstrated the possibility of reversible formation and disappearance of graphitic domains in carbon materials under hydrogen loading at low temperatures.
The main aim of this doctoral project is to investigate which properties of the carbon material (including impurities of metal and/or carbide in the carbon) and detailed conditions of experiment (H2 pressure and temperature) are crucial to bring forth the discovered reversible structural change in carbon. The doctoral project involves the synthesis of carbon materials series from carbide and peat precursors with properties necessary to bring forth the process under investigation. The carbon materials are thereafter characterised with various lab-scale methods (gas adsorption, X-ray diffraction, Raman spectroscopy, mass spectroscopy etc.). The formation/disappearance of graphitic domains will be investigated with various in situ neutron scattering methods (neutron powder diffraction, quasi-elastic neutron scattering and inelastic neutron scattering). Finally, the important aim of the doctoral project is to determine how the process of reversible structure change of carbons could be used for different high-technology industrial applications, e.g. cost-effective hydrogen storage, isotope separation etc.
Supervisors: Kaido Tammeveski, Margus Marandi, Marek Mooste
Currently, one of the limitations for the large-scale commercialisation of the fuel cells is the use of Pt-based oxygen reduction reaction (ORR) catalysts. Therefore, an alternative non-precious metal catalyst material is needed for the replacement of Pt-based cathodes. Recently, the use of conducting polymers for the preparation of platinum group metal-free materials for fuel cell applications has grown due the attractive properties of the conducting polymer-derived ORR catalysts (CPDC) such as low-cost and facile preparation routes. For tuning the properties of CPDC materials towards higher ORR activity, doping with heteroatoms (e.g. S, N, P, B) and transition metals (e.g. Fe, Co, Mn) can be performed. The aim of the present PhD study is to prepare the sulphur and nitrogen codoped catalysts using the pyrolysed polymer blends for this application. To study the influence of the morphology of polymer materials on the ORR activity, chemical and electrochemical polymerisation methods will be both used. The electrochemical polymerisation is generally preferred for better control over the polymer properties. In present work, the environmentally greener solvents (e.g. water) will be preferred as the polymerisation medium. In addition, the inclusion of transition metals into the polymer structure will be employed to prepare highly active M-N4 type ORR-active centres into the pyrolysed polymer material. A thorough electrochemical and physical characterisation of the pyrolysed materials will be carried out together with the fuel cell testing.
Supervisors: Agnes Kütt, Ivo Leito
The field of Weakly coordinating anions (WCAs) has been of significant interest, the corresponding class of compounds, weakly coordinating cations (WCCs) have only recently started to attract more attention when cleaner and higher yield procedures are sought. The main purpose of the present project is to design and synthesize such cations. Electrophilic cations are included which are the basis on the reactions of frustrated Lewis acid-base (FLP). The present work is based on synthesizing new type of compounds, including superbases and working out new synthesis methods. Participating in present work, the student obtains necessary skills on synthetic organic chemistry.
Supervisors: Hanno Evard, PhD; Darja Lavõgina, PhD
Quantitative detection of small-molecular-weight analytes in natural systems represents major challenge if the physiologically relevant concentration of the analyte is in the sub-nanomolar range, especially in case of routine analysis methods used in clinics. This PhD project aims to resolve these issues in the context of detection of oestradiol (E2), a steroid hormone with normal levels of <20 pg/mL in blood in some cases. Abnormal E2 levels are indicative of a variety of diseases (e.g. cancers). Different methods for measuring E2 exist (radioimmunoassays, HPLC/MS, GC/MS), yet the precision is still insufficient at low concentrations.
Here we plan to combine the unique advantages of 3 major techniques: signal amplification using sequential addition of nanoparticles (NP), biochemical binding/displacement assay with detection of fluorescence anisotropy (FA), and total internal reflection fluorescence microscopy (TIRF). TIRF will be combined with suitably functionalized surfaces for capture of analyte. Those will in corpora facilitate development of one combined assay, or two separate assays for the E2 detection.
This project will be carried out as collaboration between the two research groups (Microfluidics workgroup from Chair of Analytical Chemistry and the GPCR workgroup from the Chair of Bioorganic Chemistry) with necessary expertise, equipment and licences (approval from the Research Ethics Committee to conduct studies with blood) for successful accomplishment of such challenging task.
Supervisors: Nadezda Kongi, Kaido Tammeveski
The continuous rise of the atmospheric carbon dioxide (CO2) concentration is of significant global concern. Several technologies are proposed and applied by the industries to mitigate the emissions of CO2 into the atmosphere. Electrochemical processes can have significantly higher efficiencies than traditional thermal-swing and pressure-swing technologies because they operat at near isothermal conditions. Electrochemical CO2 separation can be achieved when the adsorbents are activated at some applied potential, and carbon dioxide can be released when the polarity is reversed. In this doctoral project we will develop novel redox-active MOF-based materials with high electrochemical affinity to CO2. Redox activity of the prepared and optimised MOF-based materials will be assessed by electrochemical techniques such as cyclic voltammetry and in-situ solid-state spectroelectrochemical techniques. Due to their unique physicochemical properties and their engineerable structure MOFs are a promising class of advanced materials that are creating new possibilities for electrochemical carbon capture.
Supervisors: Siim Salmar, Mart Loog
Lignin is the largest renewable resource for aromatic compounds in nature, and its valorization is a high-potential alternative to petroleum products. Currently, the main sources of technical lignin are conventional pulp mills, which produce Kraft lignin (KL) and lignosulfonates. These products are mainly used to produce cheap energy. In the near future, however, the main suppliers of technical lignin will be sugar/biofuel biorefineries, where wood is fractionated into sugars and native lignin. The predicted production volume of the native lignin is > 200 Mt / year that is two orders of magnitude higher than the maximum forecast of KL production. It is clear that lignin production optimization and development of high-value utilization methods are the game-changers to make biorefineries economically more successful in Estonia and worldwide.
Lignin valorization through depolymerization/decomposition, which is currently the prevailing approach, is challenging as the product is a very complex mixture of compounds. The main objective of this PhD project is to valorize biorefinery lignin to appropriate products directly, using its polymeric form. This goal will be achieved by developing methods of purification and fractionation of crude biorefinery lignin to obtain reactive macromolecules and their implementing in various chemical and physical approaches. Besides that, the aim of the project is to develop methods for advancement of polyurethan, polyphenol, polyepoxy, etc. materials by using fractionated lignin macromolecules.
The research and development tasks, planned in this doctoral project, are consistent with the general objectives of the Core Laboratory for Wood Chemistry and Bioprocessing of Tartu University and contribute to the development new lignin technology as a part of modern and sustainable wood processing technology.
Supervisor: Ivo Leito
The recently introduced unified pH (pHabs) concept removes one of the main drawbacks of the conventional pH (IUPAC pH) – separate, non-comparable pH scales in different solvents. The main advantage of the pHabs scale is that pH values measured in any solvent become directly mutually comparable (in terms of the thermodynamic activity of the solvated proton). Because of this, pHabs might revolutionize the way we measure and interpret pH in non-aqueous and mixed solvents, including solvents with low polarity. This concept has been for the first time experimentally realized at UT and up to now pHabs measurements are done only in very few places in the world. Measuring acidity as pHabs is potentially highly advantageous in catalysis, liquid chromatography, sustainable energetics, rationalization of acid-base processes, etc. The PhD project aims at advancing the measurement methods, especially in low-polarity solvents, and demonstrating the usefulness of the concept on exemplary applications.
Supervisor: Ahmed Awad
Conformance checking is a sub-field of process mining. Conformance checking is concerned with measuring how a process instance conforms to a predefined process model. In offline settings, this is achieved by either token replays or alignments (the state of the art). The computation of an optimal alignment is expensive. In an online settings, the computation of alignment has to be done incrementally as the a running process instance (case) evolves. Additionally, this has to happen under latency and resource constraints. Another challenge is to handle stream imperfections. One form of these imperfections is the out-of-order arrival of case data (events).
The main goal of this doctoral project is to develop techniques for adaptive and self-correcting online conformance checking. Self-correcting comes as a response to the possibility of out-of-order arrival of events. That if left uncorrected, can lead to serious misinterpretations and false negatives about the conformance status of a process execution to a predefined process model. Such self-correcting technique, additionally, have to be efficient to work under low latency constraints expected in a streaming context.
Supervisors: Radwa El Shawi, Stefania Tomasiello
The goal of this doctoral work is to introduce an interactive and explainable automated unsupervised framework for data clustering. Nowadays anyone who tries to cluster data, whether a data-mining expert or a common user, is faced with an unclear decision over which algorithm to use and how to set the cluster hyper-parameters. It is extremely difficult for users to decide which algorithm would be the best choice for a given set of data. Currently, many data scientists choose a particular algorithm rather for its speed or thanks to their previous experience with that algorithm on a completely different problem. Cluster analysis is typically employed in the initial phase of exploring raw data, where prior knowledge is minimal. Having automated methods is crucial, especially in the modern era of “big data” where manual data investigation would be overwhelming. The framework should empower its users to develop their own satisfying and trusted models. It should reduce the burden on for data scientists and domain experts for going through the time-consuming process of building and deploying scalable machine learning models.
Supervisors: Raul Vicente, Eduard Barbu, Jaan Aru
Due to their black-box nature, the top-performing AI models are difficult to interpret, and hence trust. Explainable AI is a pressing issue and a major bottleneck towards efficient human-machine interaction. Here, we propose to formalise how humans produce and rate causal-based explanations to simplify and provide human-like explanations of popular machine learning models. The main goal of the project is to incorporate behavioral and cognitive models of human explainability and causal reasoning in providing contrastive explanations (why P rather than Q?) and connecting causes to their main effects. Because large causal chains are usually intractable by humans, the behavioral and cognitive models should allow to select, connect, and present the causes (and their counterfactuals) more relevant for human-level explanations. By instantiating the output of the learned expressions in particular cases, these models will answer queries from the human, such as why a given solution was chosen, or why another solution was not. Local comprehension is key for the human to interpret the model outcomes and reach global comprehension. These models fit the human need for satisfactory and comprehensible explanations and will help humans to better understand decisions made by artificial intelligence systems.
Supervisor: Raimundas Matulevičius
Intelligent infrastructure systems are described as complex socio-technical systems. They use sensors, networks, and processing functions to collect, transfer, process, analyse, and support decision making and assist human activities. However, such a system could suffer from various security and privacy risks. Privacy leakage may impact a person’s safety, health, and well-being. This project aims to develop a systematic approach for security risk and privacy leakage management in intelligent infrastructure systems. The approach will describe a protected system and business assets from different (static, functional, and behavioural) viewpoints. It will characterise security threats and privacy leakages at the perception layer, network, and application layers of the intelligent infrastructure systems. It will reason the selection of countermeasures (security and privacy requirements and their controls) to mitigate identified security risks and data leakages in the intelligent infrastructure systems.
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 very often rely on centimetre-accuracy GNSS for localization. Map-based localization techniques exist for autonomous vehicles, but they employ expensive multi-beam lidars, precise maps, and have only been investigated for limited geographical areas and mild, unvarying weathers. All these limitations render them impossible to scale for large-scale commercial deployment of autonomous vehicles.
The proposed project aims to investigate map-based localization using cost efficient vision sensors. It intends to explore localization using semantic information such as traffic signs and information on road geometry and infrastructure. This is done with applicability to a national scale (rather than a neighbourhood or a city), and invariance to changing weathers at different times of the year, which is especially important in the Estonian/Nordic context.
Expected outcome of the projects includes feature definitions and algorithms for map-based localization on a national scale and varying weathers. Such methods will be a significant contribution towards level-5 autonomous driving, especially as machine-learning based “end-to-end” approaches in autonomous driving mature.
Supervisor: Jaan Aru
To deal with the ever-changing world, an adaptive system needs to combine pieces of knowledge to solve the task at hand. The first step of that process is learning the pieces from the data. Recently there has been a surge of work on how these knowledge pieces are disentangled from the entangled data. However, a second necessary step is to recombine these disentangled representations to tackle the problem in front of the agent. What are the algorithms for such recombination? The obvious problem is that in a system like the human brain there are millions of representations that in principle could be combined, yet only the relevant ones are selected. How? This is very much an open question and the question we will tackle in this project.
To investigate this question we will combine experimental research in humans with modern artificial intelligence (AI) algorithms. We will build AI systems that have disentangled representations as their basis, create new stimuli to probe these AI systems and compare their performance to that of humans confronted with similar stimuli. By studying which algorithms are used to select and combine knowledge pieces our research will significantly contribute to understanding the processes underlying human intelligence and building AI algorithms that make use of this algorithm.
Supervisors: Fredrik Milani, Alexander Nolte, and Marlon Dumas
Process Mining techniques use event logs from business process executions to discover and analyse business processes. Recently, such methods have been combined with machine learning to produce recommendations on what actions to take to increase probability of desired process outcomes. This approach is called prescriptive process monitoring. The usefulness of a prescriptive process monitoring tool depends on whether or not the process workers are willing to follow the tool's recommendations. There has been little research on how prescriptive monitoring outputs can effectively be communicated to process workers. To address this gap, this project aims at (1) developing an understanding of the factors and data required for process workers to accept and follow the suggestions and (2) develop a model-driven visualization for prescriptive process mining that satisfies the needs of the process workers.
Supervisor: Rajesh Sharma
The aim of this doctoral project is to propose an Artificial Intelligence (AI) based framework for detecting misinformation on online social media platforms (such as Twitter). In particular, we will focus on fake news and rumors, the two main kinds of misinformation. 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 problem of misinformation detection on social media platforms has been analysed using classical machine learning and deep learning approaches. However, these solutions failed 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. To exploit the network aspect, we will employ graph neural networks (GNN) - an emerging and promising domain that lies at the intersection of graph theory and deep learning. In particular, we start with creating a taxonomy of misinformation and identifying characteristics of various forms of misinformation. In addition, our framework will include the mixed-method approach based on crowdsourcing by involving surveys and questionnaires. The output of user studies will help in making our models better and ultimately demystifying the reasons and process of misinformation diffusion.
Supervisor: Riccardo Tommasini
The World Wide Web, during its evolution, has gone through many radical transformations and expansions. One of the significant transformations is shifting from a pure human-to-human to a machine-to-machine (e.g., Web of Things and producer/consumer), where applications can communicate and evolve independently. A Significant obstacle to this latest evolution is the heterogeneity that concerns systems and data. Moreover, the dynamic nature of Web resources calls for real-time data access and processing. Because of these reasons, new protocols were designed, expanding beyond the static nature of HTTP and the ‘client-server’ paradigm, to satisfy the need for decentralised (i.e. agents) and distributed (i.e. clusters) data streams-based processes. Stream Reasoning is the research area that aims at addressing these challenges., extending the Semantic Web stack with Stream Processing (SP) abstractions. To this extent, continuous extensions of SPARQL were proposed to analyse streams of RDF data. Nevertheless, both new protocols and Stream Reasoning solutions perpetuate the same assumptions, i.e., client-server HTTP-based interaction as well as a static nature for web resources. This PhD project aims to investigate an alternative Web, designed to interact and represent ephemeral and time-varying resources. To this extent, the candidate will extend both in the Web architecture and the semantic web stack with the necessary abstractions to deal with dynamic resources.
Supervisors: Stefania Tomasiello, Evelin Loit
Uncertain and dynamic factors, such as climate change, pandemics, may threaten the environmental and economic sustainability of current and future food supply systems. Scientific and technological solutions are needed to improve the resilience and the sustainability of the system, in order to secure enough food for a fast-growing global population.
In such a context, the availability of suitable tools to assist the decision-making process is critical. Soft decision making is based on some formalisms to take into account the uncertainty and imprecision (e.g. fuzzy sets, rough sets, interval analysis) jointly with bio-inspired computing schemes (neural networks, evolutionary techniques).
The objective of this research is to successfully include and process information on qualitative criteria, while keeping the overall evaluation reliable, meaningful and transparent, resulting in an interpretable decision-making framework.
Supervisors: Kaido Reivelt, Jari Lavonen
Most physics related interest studies are based on surveys or interviews, which give insight to students’ personal interest. However, surveys and interviews have limitations because they obtain retrospective measures of students’ reports on their interest. There are only few studies that have examined the situational aspect of student interest in physics learning. Also, interest studies pay limited attention on how individual students experience interest in various learning situations. Moreover, basic assumptions that females are less interested in science learning, especially physics, are typically based on surveys instead of measurements in real situations.
In this project three project based teaching units in the context of optics will be developed together with physics teachers from four schools. We study how frequently the students engage in different activities during the designed teaching units, what is the level of situational interest in those activities and how activities, student gender and student personal interest in science studies and careers predict situational interest in physics learning situations.
Supervisors: H. Kasuk, V Ivaništšev, R. Mamlok-Naaman
The project focuses on the „teaching of energy“ with the aim to build a solid ground for training future specialists for resolving Estonian state-level challenges: the need for training science teachers, the need for updating education curricula, the need for training workers for implementing novel technologies in sustainable energetics.
To deal properly with alternative energy sources, the students, who are the future citizens, should get a better understanding of the "energy" concept (National Research Council, 2012). Their knowledge should promote their ability to learn more about several phenomena, hoping that it will develop their critical thinking and shape lives in future generations (e.g., climate, water resources, powering society, etc.).
In the first step of the study, the misconceptions in concept of energy are monitored by carrying out the primary school questionnaire by questioning the teachers and the students. Using the questionnaire's data, the new “energy module” is proposed to provide an original teaching model for explaining and promoting sustainable energetics via the STEM approach. The new concept is tested with group volunteer teachers and later with students.
Supervisors: Miia Rannikmäe, Regina Soobard, Jari Lavonen
The research project is targeting issues in science education related to irrelevance of school science in eyes of students and due to that lacking students choosing science related careers. The goals of the project are a) to determine the impact of student perceived motivation, self-efficacy and STEM-career awareness on cognitive learning for STEM competence through undertaking secondary data analyses based on e-testing across school levels; b) to develop a theoretically justified framework and interventions for promoting STEM- career awareness in transdisciplinary settings alongside conceptualization of core ideas within STEM subjects among grade 10-12 students and c) to develop analytical strategies for determining students’ understandings about core ideas in science. The target group for research are gymnasium students from grade 10. A novel longitude intervention will be designed through 2 years to promote science competence development, science career awareness and students` motivation to learn. Science competence will be measured through e- tests and based on multilevel analyses theoretical model for intervention design will be proposed.
Supervisors: Veljo Kisand, Lauri Vares
In order to tackle the environmental and climate crisis, a rapid transition from fossil-based industry into more sustainable one, based on renewable raw materials, is needed. One area needing new sustainable solutions is a plastics and polymer industry. In recent years, several new biomass-derived materials have been developed, with the aim to replace the current fossil-based plastics. However, the ecological footprint and life-cycle of these new materials need to be evaluated carefully, we need to be certain that these new biomass-derived materials have significantly lower environmental impact. In this multidisciplinary project a PhD student will investigate the environmental impact of recently developed biomass-derived polymers and materials. Degradation of novel polymers (e.g. polycarbontes, polyacrylates, etc) in natural environment will be measured and the impact of degradation products on various natural microorganisms will be evaluated. Various contemporary analyses methods, like isotope- and molecular methods will be applied, accompanied with advanced data analysis. This project gives us knowledge whether the new biobased plastics are more environmentally friendly compared to their fossil-based counterparts.
This research is part of Estonian-Norwegian-Lithuanian Project „Novel high-performance polymers from lignocellulosic raw materials“ (2020 - 2024).
Supervisors: Kaspar Valgepea, Kristina Reinmets
Rapid transition towards renewable carbon-free energy production is key for averting irreversible climate change. Still, renewable feedstocks for the sustainable production of carbon-based fuels and chemicals are also needed for a circular bioeconomy. Gas-fermenting acetogen bacteria have emerged as attractive bio-catalysts for the conversion of waste feedstocks (e.g. syngas [CO, H2, CO2] from gasified biomass, industrial waste gases) into value-added products using gas fermentation. However, better understanding of acetogen metabolism is required to advance their rational metabolic engineering. This project aims to elucidate key aspects of acetogen metabolism through combinatorial genome editing and systems-level genotype-phenotype quantification. We predict that such an integrative approach will advance understanding of acetogen metabolism and accelerate metabolic engineering of cell factories.
Supervisor: prof. Mart Loog
One of the most promising development in synthetic biology is the concept of cell factories. By redesigning genomes and cellular metabolism, one can create microbial cells that produce a wide range of chemicals and pharmaceuticals. However, since it is hard to predictably design the living systems, there is a pressing need for programmable synthetic parts and regulatory circuits for cell factory construction. Our discovery of a unique multisite phosphorylation mechanism for cellular signal processing has led us to develop a multisite phosphorylation toolbox for synthetic circuit design (MPToolbox). In this project, we will use the MPToolbox to achieve coordinated balance of biosynthetic pathway enzymes in yeast cell factories with the aim to increase the performance of the strains and mitigate toxic intermediates. A toolbox of synthetic parts based on multisite phosphorylation would revolutionize the field because of the fast response time-scales and wide combinatorial possibilities.
Supervisors: Saoni Banerji, Alvo Aabloo
State-of-the-art simulation techniques in healthcare either rely on animal and patient tests or learning and practicing complex manual skills (for example, cardiac catheterization) by technological means⸻creating systems that forsake biomimetic realism in favor of kinetic realism. This poses the need to replace the existing practices by the standardized medical procedures⸻implementing alternative systems that are compatible for integration with human tissues and have the ability to replicate complex anatomical and physiological systems: programmed to respond to the user's inputs. Soft polymeric materials such as ionic electroactive polymers⸻IEAPs are a promising choice, owing to their compliance and unique mechanical properties. These features allow for continuous and localized deformation, which are particularly interesting for integration with human tissues. Additionally, biocompatibility and biomimicry are key aspects of such materials that allow mimicking the complex dynamics of biological systems. Despite their favorable properties, there is still a significant gap separating their current state-of-the-art from industrially reliable and medically viable systems⸻lack of consistent, reproducible, and scalable manufacturing methods. Three-dimensional (3D) printing is an excellent alternative to traditional manufacturing methods for such functional materials, allowing higher functionality, controllability, and flexibility in their design.
This Ph.D. project aims to exploit the versatility and dynamic functionality of 3D printing (material extrusion) to fabricate EAPs with higher reproducibility, consistency, customization of geometry, and reduction in manufacturing time. The technique will be validated and demonstrated with a 3D-printed active catheter guide with a vision to facilitate smart catheter guides for complex coronary interventional planning for surgeons. Additional validation of the technique will be demonstrated using a tissue-mimicking active simulator⸻an EAP-based soft robotic sleeve as a ventricular assist device. The novel soft robotic technology envisions to help surgeons improve their accuracy, predictability, and repeatability while restoring the intuitiveness of the procedure and for training specialists in high-fidelity healthcare simulation scenarios.
Supervisors: Hannes Kollist, Rainer Waadt, Dmitry Yarmolinsky
Plants use light energy, CO2 and water for producing organic matter and many natural products required by the humankind in form of food, fuel and fibre. Climate change, i. e. the elevations in ambient CO2, temperature and drought periods can have a negative impact on plant yields, growth and development. Guard cells are pairs of cells that form stomata, small pores on the leaf surfaces, that are required for CO2 uptake for photosynthesis and water transpiration for temperature control and nutrient uptake. Both, elevated CO2 and drought induce stomal pore closure to prevent water loss. In recent years we have obtained significant knowledge about the molecular mechanisms that regulate stomatal dynamics, including the function of ion transporters and channels and the role of hormones and second messengers such as abscisic acid, Ca2+, reactive oxygen species (ROS) and H+ (pH). One particular ion transporter that plays a major role during light-induced stomatal opening is the plasma membrane localised proton (H+) pump AUTOINHIBITED H+ ATPASE 1 (AHA1) also known as OPEN STOMATA 2 (OST2) which will be one of the main subjects of the proposed PhD project. In previous work using Arabidopsis plants, we have identified classical and new mutant alleles of the AHA1 gene that likely result in hyperactivation of the pump and prevent CO2-, darkness- and ozone-induced stomatal closure. In the proposed PhD project, we aim to obtain deeper insights about the effects of these mutations and of regulatory proteins on the activation mechanism of the proton pump. In addition, we will generate guard-cell specific higher order mutants of the AHA gene family to study stomatal environmental stress responses in the absence of proton pump activity. Finally, we will employ fluorescent biosensors to study the effects of altered proton pump activity on ion-and second messenger dynamics in guard cells. Overall, this PhD project aims to decipher the regulatory mechanisms and functions of proton pumps during environmental stress-mediated stomatal responses.
Supervisors: Gholamreza Anbarjafari and Chagri Ozchinar
Cosmic-ray tomography (CRT) is considered as beyond the state-of-the-art technology in cargo screening. Cosmic rays interacting with the atmosphere results in the flux of secondary particles including muons and electrons. Atmospheric ray tomography (ART) uses the muons and electrons for detecting objects and their composition. This PhD proposal is focusing on developing a breakthrough multi-stage deep neural network that can be used in classification of 3D objects that have been reconstructed using ART based scanning device. We will use synthetic data generated by Geant4 models as well as real data collected by GScan owned ART scanner in order to train and test our development throughout this PhD work. We will investigate the special and temporal resolution for our algorithms. This will provide a feedback in designing of next versions of such scanners. The work will initially working on recognition of explosive and narcotics and will continue by recognition of low-Z materials. Additionally, we will investigate how we can introduce recognition of a 3D object based on each layer of scan. This will simulate a recurrent networks based object classification.
Supervisors: Reet Kurg, Margit Mutso
Methylation is an essential epigenetic modification mainly catalysed by S-Adenosyl-l-methionine dependent methyltransferases. TRMT112 is a small evolutionary conserved protein that acts as a cofactor for several rRNA, tRNA and protein methyltransferases. Data from previous research show that TRMT112 interacts and forms a complex with at least 7 different methyltransferases. Furthermore, there is a positive feedback loop and mutual stabilization between TRMT112 and its partners within the cells. The aim of this PhD project is to study the TRMT112-methyltransferase network and elucidate the functions of its partners. During the project a cell-based assay will be generated, which would allow to individually disrupt the interactions of some of the TRMT112-methyltransferase complexes to study others in more detail. Recent studies show that abnormal methylation patterns are linked to numerous diseases, therefore it is important to study methyltransferases as they could be used as new drug targets.
Supervisors: Tanel Tenson. Ülo Maiväli, Arto Pulk
The ribosome is probably the most complex macromolecular machine in the cell. This makes it an interesting subject for research not only into its function (translation), but also into how it is assembled. This project aims to reap the benefits of recent great advances in cryo-electron microscopy, to solve high resolution structures of native ribosomal assembly intermediates bound to assembly factors. This will be combined with quantitative description of protein compositions of these particles achieved by mass spectroscopy, and with genetic selection experiments geared for identification of hitherto unknown partners of known ribosomal assembly factors (the proteins that bind to ribosomal assembly intermediates and catalyse the process of assembly). In conclusion, we aim by combining structural studies with functional ones to better characterize ribosomal assembly as a dynamic directional process.
Supervisors: Andreas Kyritsakis, Vahur Zadin, Reet Kurg
Extracellular vesicles (EVs) play a vital role in intercellular communication and are of crucial importance for various novel biomedical applications, especially cancer diagnostic methods such as liquid biopsy. In order to effectively utilize EVs as biomarkers for diagnostic purposes, highthroughput purification methods are required. Microfluidics is a very promising approach, giving a unique opportunity for high throughput processing and offering the ability to utilize additive manufacturing (3D-printing), resulting in a device with an automated and inexpensive fabrication procedure. In this project we develop a novel microfluidics high+hroughput 3D-printed device that selectively purifies EVs from biofluids, utilizing a combination of size separation and multiply selective altemating current (AC) electrophoresis. Since EVs are negatively charged, their movement within a liquid solution can be controlled by an external field. This property will be used to separate EVs from bioliquids by applying an electric field along multiple microfluidic channels perpendicular to the motion of the fluid. By controlling the cross section size and the applied voltage ofeach channel, a highly selective and effective separation shall be achieved. In order to predict the function and effectively optimize the device, experiments and fabrication shall be preceded by computer simulations. Such simulations include the calculation of the fluid flow, concurrently with localized mass and heat transfer, and solution of the electrostatics equations for the electric fields, using the finite element method and the Multiphysics software Comsol, which is suitable for combining advanced multi scale and multi physics phenomena. After developing this simulation model, a prototype device will be fabricated and optimized in order to reach the desired performance.
Supervisors: Alvo Aabloo, Ville Viikari
To summarize, recent studies show that mutual coupling between antenna elements can be beneficial. In addition, the load impedance affects the operation of a power amplifier and its performance can be optimized by choosing the proper load impedance for the given goal. An antenna array with mutually connected elements makes load control possible through the active impedance concept. Our hypothesis is that an active antenna array can be made significantly better by intelligently designing the driving amplifiers with the array and by accounting the active impedance effects of the antenna array properly. We assume that such an approach can be used to 1) widen the operation band of the active antenna array, 2) extend its beam steering range, 3) improve the linearity, efficiency, or gain, and 4) realize highly efficient output power or gain control.
Supervisors: Kaupo Kukli, Aile Tamm, Jekaterina Kozlova
Resistive switching phenomena in different dielectric multilayers will be investigated during the doctoral studies. The goal of the studies is to seek and find multistate structures most suited to the possible applications in computer memories to store and elaborate data and information. During the PhD studies structure with optimized parameters will be selected, in order to thoroughly explore electrical properties of the multilayer stacks and examine the performance of memory cells. Design of memory matrices with high packing density will be aimed at, when selecting new materials for this purpose.
Supervisor: Indrek Must
Soft robotics promises a physical intelligence layer for compensation of mobility impairments and heahh improvement. In pursuit of a body-mirroring exorobot (in contrast to implants) to be worn by humans, soft robotics already offers softness, compliance, as well as energy density. The existing best prototypes of exorobots exemplify that these qualities are not sufficient - a practical wearable robot that satisfies market expectations needs a further multidisciplinary approach. Already being in good command of soft and compliant materials, this project tackles architecture to bring all pieces of the puzzle together. This project takes a body-mirroring approach, inspired by natural vascular systems that promise drastically improved power density for wearable exorobots. In addition to the definite benefit to the public health and wellbeing, the wearable robot technology offers a nav market opportunity for traditional textile industry.
Supervisors: Ebe Merilo, Hannes Kollist, Dmitry Yarmolinsky
Crop yield is determined by incident light on one hand and by harvest index and the efficiencies of light capture and light conversion efficiency on the other hand. During plant breeding, harvest index and light interception have reached values close to their theoretical maxima. Thus, improvements in net assimilation rate (photosynthesis) may represent the only real option to attain higher crop yields. Increased photosynthesis together with higher resource (water, nitrogen) use efficiency have been highlighted as important breeding directions in the future climatic conditions. The aim of the PhD project is to study the variation and environmental sensitivity of net CO2 assimilation rate and stomatal conductance in different transgenic and conventionally bred barley and wheat lines. The PhD project is linked with three European projects aiming to assist and accelerate plant breeding to develop cultivars better adapted to future climate conditions. The variation and sensitivity to abiotic factors related with climate change (ambient CO2 concentration, VPD) of net assimilation rate and stomatal conductance will be characterized in the field and laboratory. The results of planned experiments together with climate and yield data from the field site allow to clarify the breeding potential of gas exchange traits in order to increase crop yield.
Supervisors: Alla Piirsoo, Marko Piirsoo
Inhibition of human papillomavirus (HPV) replication is a promising therapeutic approach for intervening with HPV-related pathologies. E1 and E2 are the only viral proteins required for HPV replication. Adjustment of E1 and E2 biological activities to support replication of the viral genome relies on recruitment of diverse host cell factors including protein kinases. However, the functional impact of the E1 and E2 post-translational modifications on HPV replication and/or protein kinases involved in the E1 and E2 regulation remain largely uncharacterized. This PhD project aims at identifying protein kinases directly involved in regulation of E1/E2 proteins during different stages of the high-risk HPV genome replication with further goal to develop approaches for inhibition of HPV replication via manipulating activities of the identified protein kinases.
Supervisors: Hanna Hõrak, Ebe Merilo
Photosynthesis is the basis for plant biomass production. Photosynthetic rate and ultimately yield depend on CO2 uptake into the plant, mediated via stomatal pores in leaves. Higher stomatal numbers (stomatal density) and larger apertures allow faster photosynthetic rates, but drawbacks are increased water loss via transpiration, low water use efficiency (WUE) and susceptibility to drought. It is possible to increase WUE via reduced stomatal numbers and apertures, but this tends to limit photosynthesis, whereas improvement of photosynthesis via increased expression of Calvin-Benson-Bassham cycle enzymes leads to higher stomatal water loss and reduced WUE. Alteration of both WUE and photosynthetic efficiency via simultaneous modification of stomatal traits and photosynthesis may help to overcome this trade-off. During this project, model plant lines (Arabidopsis thaliana) that have altered stomatal numbers and apertures and increased expression level of photosynthesis-boosting enzymes will be generated. The stomatal traits, photosynthesis, WUE, biomass production and yield of the generated lines will be characterized during the project. The results will give insight into the interaction of stomata and photosynthesis and answer the question, whether it is possible to have it all – high WUE and improved yield – thus providing valuable input for smart crop breeding.
Supervisor(s): Gholamreza Anbarjafari (Shahab) and Chagri Ozchinar
Cosmic rays interacting with the atmosphere results in the flux of secondary particles including muons and electrons. Atmospheric ray tomography (ART) uses the muons and electrons for detecting objects and their composition. This PhD proposal is focusing on developing a breakthrough multi-level deep neural network that can be used in reconstruction of 3D objects that have been scanned using ART scanning device. The reconstruction includes steps such as denoising, edge detection, and mesh fitting. For the experimental results, we will initially use synthetic data generated by Geant4 models and later on we will focus on real data collected by GScan owned ART scanner. We will investigate the limitation that the algorithm can introduce and we will use this feedback for improving the design of scanner as well. The main information used for creation of point clouds will be the scattering angle and the ratio of absorption which will be calculated.
Supervisor: Kaido Soosaar
Greenhouse gases (GHG) emissions caused by human activities are the most significant driver of the observed climate changes since the mid-20th century. Managed nutrient-rich organic soils are among the largest sources of GHG emissions from the Agriculture and Land Use, Land Use Change and Forestry sectors in boreal and temperate cool and moist climate regions in Europe. However, the amount of data available on actual GHG emissions from variously managed nutrient-rich organic soils in Estonia and the Baltic States is insufficient. Greenhouse gas fluxes from drained organic soils generally depend on site nutrient status, vegetation cover and local environmental parameters such as soil temperature and the groundwater table regime. Drainage and land-use changes enhance decomposition, and organic soils, especially drained nutrient-rich ones, can turn into net GHG sources. GHG emissions from drained peatlands can be decreased by introducing management changes that shift organic soils towards a GHG sink state.
This PhD project's general goal is to improve our understanding of GHG exchanges in drained nitrogen-rich peatlands with different land-use – forest (different tree species), grassland, cropland and wetland – under various environmental conditions. Separate component fluxes (i.e., heterotrophic respiration, production and consumption of N2O and CH4 from soil and stems) in response to land use and environmental controls will be measured.
The study aims to increase the knowledge base for the quantitative assessment, projections and implementation of the most effective climate change mitigation measures to manage nutrient-rich organic soils. The thesis also intends to improve the GHG models and carbon and nitrogen balance of drained nutrient-rich peatland ecosystems.
The fieldworks will be focusing on GHG flux and environmental parameters measurements in recently established LIFE OrgBalt study sites in Estonia. In the case of forest sites, GHG flux will be measured simultaneously from soil and tree stems. All the collected data stored in a joint LIFE OrgBalt project database (includes the data collected from the study sites in Estonia, Latvia, Lithuania, Finland within LIFE OrgBalt project) will be used in the current PhD student. GHG flux data from the database will be joined with detailed soil and water biogeochemistry information to improve process-based modeling of peatland GHG emissions further. Joint database data will be used in the current PhD study for the comparative analysis.
Supervisors: Krista Alikas, Mariano Bresciani, Claudia Giardino
The proposed study and research activity deals with the overall topic of remote sensing for aquatic applications. It focuses on the exploitation of the new generation of hyperspectral missions to produce water quality indicators. In addition to the existing multispectral satellite sensors of the ESA’s Copernicus mission as well as international programs (e.g., NASA-USGS Landsat), the latest satellite imaging spectrometry missions now offer the possibility of integrating monitoring activities and are complementary to the existing satellite and in-situ measurements. The main objective of this study and research activity will therefore be to investigate and understand the advantages and critical issues of these modern sensors. In particular, the research activity will focus on the analysis and processing of satellite images of aquatic ecosystems covering a wide range of water types, encompassing coastal and inland waters. The areas under study will included but not limited to target areas included in the supporting projects and that are distributed worldwide. In particular for the freshwater reservoirs of Lake Mulargia (IT), Lake Harsha (US), Lake Hume (AU), Melbourne Western Treatment Plant (AU) (H2020 PrimeWater); the major Italian lakes (ASI-PRISCAV) and the large Estonian lakes (e.g., Peipsi lake and Võrtsjärv lake) (H2020 Water-For-CE). The activity will include the use and analysis of radiative transfer codes to remove disturbances due to the presence of the atmosphere from the satellite signal. Dedicated algorithms featuring the hyperspectral setting of imagery data will developed and tested for estimating both the bio-geophysical parameters of the water column (e.g., concentration of chlorophyll-a and secondary phytoplankton pigments, total suspended solids, dissolved coloured organic substances, the study of evaporation and primary production) and of shallow water (e.g., depths, substrate type). The activity will allow to optimize the use of the most performing codes and algorithms with respect to the remotely sensed parameters and different water types met in the investigated case studies. In parallel with the analysis of satellite images, in-situ measurement campaigns will also be carried out: these campaigns will be dedicated to the collection of radiometric and bio-geochemical data to support the validation of satellite products. Overall, this study will allow to booster the use of RS technology in a wider ambitious frame. This research might contribute to the space-time monitoring of the state of water in accordance with the European directives (e.g., EU Water Framework Directive) as well as in view of the achievement of the Sustainable Development Goals (SDGs, or Agenda2030).
Supervisor: Krista Alikas
The aim of the thesis is to analyse the uncertainties present both in the field and satellite remote sensing data and to advance and develop new methods in aquatic remote sensing to move from multi-spectral imagery towards hyperspectral for improved analysis of optical water quality. By the means of fiducial reference measurements, it can be assured that only the data with low uncertainties will be used to validate the regional and global satellite products over eutrophic lakes to indicate the suitability and accuracy of algorithms. With the current operational hyperspectral missions PRISMA, both NASA and ESA are developing new hyperspectral missions usable for water quality monitoring. This is a major step forward from the multispectral missions started from the 1978 and continued until now. New hyperspectral data allows to apply and test methods and approaches to improve the detection of optically active substances in water by the means of remote sensing data and also allows to explore the possibilities to derive novel ocean colour products e.g. particle size class, spectral slope coefficients for inherent optical properties.
Supervisors: Maido Remm, Lauris Kaplinski
More than a half of the human genome is made up of repetitive sequences. Repeated sequences may be transposed eavenly throughout the genome, or in tandem often with a unique motif in the genome. Genomes tend to grow larger due to repetitive sequences, so they are polymorphic in nature and multiallelic in tandem repeatitions. The identification of repetitive sequences and the identification of alleles is difficult, so their association with phenotypic traits has been understudied. The aim of this work is to map repetitive sequences in NGS data, to determine their genotype and, if possible, their relationship to the phenotype using k-mer frequencies. We have plan to use the data of the Estonian Gene Bank (https://genomics.ut.ee/en) and GTEx (https://gtexportal.org/home/) full genomes, RNAseq as well as phenotype description.
Doctoral thesis plan and planned publications:
1) For the first article, we map low and high motif copy numbers in tandem repetitive sequences (VNTR and satellite sequences, respectively) in the human reference genome and Gnomad datasets. We create two models:
a. determining the average copy number of the tandem repetition with a subset of specific k-mer frequencies for local coverage for VNTRs;
b. determining the average length of the tandem repetition with the fraction of specific k-mer frequencies relative to the global coverage of the satellite sequence.
2) We determine the average copy number of VNTRs and the average length of satellite sequences in the GTEx WGS data and determine their correlation with mRNA levels in different tissues.
3) Based on the GTEx results, we try to perform replication experiments in EGB datasets for those features that are possible in addition to the conventional GWAS.
Supervisors: Tõnu Esko, Tanel Mällo
Content Advisors: Andero Uusberg, Kalle Killar
The doctoral thesis aims to study the factors influencing a person's ability to manage the use of his or her health and genetic data in research, development and implementation of health services and the treatment of these influencing factors in national and international vision and implementing documents and regulations, using national consent management service implemented in Estonia as an example.
Nationwide implementation of the consent management service raises many issues. Among other questions, it can be presumed that by implementing the consent management service, it can be presumed that the service generates and increases the demand for health data by third parties. The solution provides a new opportunity to obtain additional (health) data and to cross-use different data. This can lead to the decline of a person’s privacy. Consent management service can lead to overprocessing of health data. Scattering databases is one of the core principles of the architecture of a state's information system. However, the consent management service implementation can lead to a situation, where data mediators appear. They will provide data analysis services or will buy data for the purpose of resale. The person might not have an overview of the actual content of the mediator’s actions.
National consent management system is unique in the world context, and the mapping of the application process will provide an important input for similar services in other countries. The Estonian Biobank (Tartu Ülikooli Eesti Geenivaramu) is the thought leadership of a unique database of 200,000 consented participants, thus having a great responsibility to gene donors to protect their privacy and interests as well as support Government initiatives in the field of personalised medicine. At the same time, genetic data is of great value to research and innovation, and good data governance and consent literacy is a crucial part of data guided healthcare models. The PhD thesis will propose measures to the data sharing process for supporting a person's ability in managing the use of their health and gene data.
Supervisors: Christiana L. Scheib, Kristiina Tambets
This project will generate whole-genome metagenomic libraries from ancient dental calculus samples in a range of European archaeological contexts (Neolithic to post-medieval) and optimise bioinformatic methods for analysing this data in the context of ancient DNA and proteins in order to better understand the impact of cultural change on human health. The student will collaborate with post-doctoral researchers who will generate shotgun proteomic data from the dental calculus samples and human host data. The first component of the project will assess differences between the Neolithic and Bronze Age populations in Central and Southern Italy. The Bronze Age transition brought both demographic and cultural change as well as a shift in the prevalence of pathology in the archaeological record. This component will assess if these shifts in ancestry, diet, pathology and culture correlate with shifts in the oral microbiome. The second component will assess differences in oral microbiome between urban and rural medieval Estonians. The third component of the project will compare the oral microbiomes of pre-Tobacco populations with smokers (in separate European cohorts: Estonia, Belgium, Netherlands, Britain and Spain). These data sources will be combined with the human (host) genomes and any detected pathogens (e.g. Mycobacterium tuberculosis) to better understand the impact of the introduction of tobacco smoking on the health of European populations after 1492.
Supervisors: Kadi Kalm, Kadri Leetmaa, Tiit Tammaru
In recent years, the linkage between segregation and gentrification processes has received a lot of attention. However, there is no clear understanding of the relationship between these two and clarification is needed about the impacts of gentrification on contemporary segregation patterns. The present PhD project contributes to this gap and questions whether and on what conditions do the displacement mechanisms of gentrification provide explanation for the social, age and ethnic homogenisation of post-socialist neighbourhoods. It aims to develop a fuller understanding of the relationship between gentrification and segregation based on a post-socialist city of Tallinn. The capital of Estonia has experienced fast social and spatial change during last quarter of a century and as a context that can be described by superhomeownership it offers a great place to study the relationship between these two processes. Data analysis is based on the individual-level census databases (1989, 2000, 2011) and recent registry data (2015-2020).
Supervisors: Garri Raagmaa, Jarkko Saarinen
In today´s media driven society films play important role raising attractiveness of a destination. Many places have gained popularity because the fans of the film want to visit the location of the shoot and experience the filmscapes in a real life. There are several humorous beloved films in Estonia “Men will not cry” (in Estonian “Mehed ei nuta”), “Here we are”, (“Siin me oleme”), “Real life of Johannes Pääsuke“ (“Johannes Pääsukese tõeline elu”) which locations of the sets are in rather remote areas. Hypothetically, their current popularity has been at least partly born from the increased visibility created by stories told in the movies.
Of course, it might happen that the places seen on screen will not provide exact landscapes in real life because of local inhabitants and their identity, so conflicts of interest between fictional servicescapes and the locals may occur. Different parties can be managed using sustainable management strategies considering identity of locals, identity of film tourists and image created via humorous film.
Supervisors: Siiri Silm
The COVID crisis raised the need more strongly than ever before to be able to assess the location and movement of people. The topic of the use of new data sources, including mobile positioning data, was also raised as an object of discussion. Mobile positioning data has been used in a number of areas to better understand the space-time behaviour of people and to better manage society. The data also provide a great potential in terms of assessing the location and movement of people in crisis situations.
The aim of this doctoral thesis is to contribute to the knowledge of people’s spatio-temporal behaviour in crisis situations, with the aim of better managing the crisis. The focus of this thesis is on the following aspects: 1) the spatio-temporal behaviour of people in a crisis situation when compared to a normal situation; 2) social inequalities and vulnerable people in crisis situations; and 3) providing a contribution to the discussion on how to use mobile positioning data and make crisis management more effective.
This thesis is based on various types of mobile positioning data: passive mobile positioning, and smartphone-based positioning, combined with interviews.
Supervisors: Mikk Espenberg, Ülo Mander
Peatlands account for ~3% of the terrestrial surface of the global land surface but they store one-third of the world’s soil carbon and one-tenth of the world’s soil nitrogen. Extensive peatlands are especially sensitive to land use and climate changes in tropics and cold-climate areas and, in addition, alterations there may cause vast amounts of different greenhouse gases (GHG) emissions. Quantity and distribution of carbon and nitrogen are controlled through biogeochemical processes; however, the lack of knowledge regarding microbial processes governing GHG emissions hinders climate-change impact estimations of the world’s peatlands. Deciphering the biogeography of peatland soil microbial communities as a whole would help to put microbes from small to broad scales. Both bacteria and archaea play important roles in networks of carbon and nitrogen cycling. Besides, less amount of attention is paid to our ability to describe the extremely diverse pool of low abundance prokaryotic populations (i.e., rare taxa) present in natural microbial communities is ambiguous. Thus, this project aims to use a unique metagenomic dataset of peatland soils worldwide to connect microbial carbon- and nitrogen-cycling processes to physicochemical parameters and GHG fluxes at ecosystem levels.
Supervisors: Ivika Ostonen, Priit Kupper, Marika Truu
Understanding and predicting how climate and land-use change affect ecosystems' structure and functioning is a crucial challenge of the 21st century. Environmental change (such as rising temperatures and humidity, drainage of wetland forests, or increasing frequency of Ips typographys attacks in spruce forests) in northern latitudes have consequences for important forest ecosystem functions, such as plant biomass production and shifts in above- and belowground allocation patterns, which may feed-back to climate through shifts in the biogeochemical cycles in ecosystems. Qualitative or quantitative changes in rhizodeposition (root litter, metabolite exudation) will cause a shift in the structure and function of soil and rhizosphere microbiome responsible for stabilizing SOM. Plant roots and their associated microbial communities play a significant role in the rhizosphere and soil nutrient cycles that need to better understood from the point of view of the sustainable functioning of forests as well as of global C cycle in soils.
The overall goal of this doctoral project is to advance our understanding of the effects of changed water conditions (drainage, irrigation, higher relative air humidity), higher temperature or higher frequency of insect’s attacks’ on trees growth and on the structural and functional adaptation of plant and soil microbiomes related nutrient cycle in the rhizosphere.
The novel tools for soil and rhizosphere solutes sampling (microdialysis) and data analysis approaches including machine learning will be applied to analyse and integrate nutrient fluxes in the rhizosphere (including interactions between plant and soil microbiome, plant traits and physiological processes and environmental conditions). Novel and advanced approaches in belowground studies help to bring the changes in plant root traits, soil microbiomes and rhizosphere C and nutrient cycles to the ecosystem level.
Supervisors: Argo Jõeleht, Enn Karro, Andres Marandi
This project aims to test coupled groundwater-surface water modelling systems in Estonia and to assess the impact of climate change on Estonian water resources. The 2015 UN World Water Development Report predicts a 50 % increase in water consumption in the world. The stress on water resources will vary from region to region and therefore, new data, models and data analysis methods need to be introduced. Many studies point on the need for integrated surface water and groundwater modelling system. The status assessment of groundwater bodies carried out in 2020 showed the growth of organic components in shallow groundwater bodies in Estonia. This may reflect the effects of climate change that are already occurring or effects of increased agricultural intensity. The project has step by step structure, where the research goes from local to more regional modelling, where the components of the model are tested first in local environments (fissured limestone, karstic area, high agricultural intensity) and later the model is applied at the regional level. The doctoral project is closely related to the ongoing projects by the Estonian Geological Survey (LIFE IP CleanEST, EUWaterres) that generate high-density surface water and groundwater quality and quantity data. As a result, specific water management recommendations will be developed as well as suggestions for more efficient use of national environmental data.
Supervisor: Peeter Somelar
Objective of this project is to understand the processes that triggered and supported the emergence of multicellular life and to reconstruct the paleoenvironmental conditions and the atmospheric composition during the Ediacaran and Cambrian transition. This period of time witnessed fundamental changes in biogeochemical cycles with the final oxygenation of the atmosphere, global glaciation and warming events, large disturbances in global C-cycle and the onset of eukaryotic diversification and the emergence of animal life. The project aims to recognize the changes in weathering intensity and regime during the Ediacaran and Cambrian transition and tries to reveal its links to changes in atmosphere composition, ocean chemistry and multicellular life. In this project, unique and well-preserved sedimentary rock successions and continental weathering crusts (i.e. denudation surfaces) from different palaeocontinents and basins across the Ediacaran and Cambrian transition.
Supervisors: Ilmo Sildos, Yurii Orlovskii, Ago Rinken and Sergei Kopantšuk
The general objective of the project is to elaborate novel fluorescent nanoparticles (NPs) for imaging, thermometry and related biomedical applications. In case of nanodiamonds the attention is focused on a few specific impurity centers (with zero-phonon lines at room temperature). The spectrally narrow and highly temperature sensitive fluorescence features are usable for local nanothermometry in biological tissues. The second type of NPs involved are rare-earth (RE) ions doped fluorides, which absorb and emit photons in the visible range and the first transparency window of biological tissues and can be used even for deep bioimaging. During the investigation of NPs their fluorescence and thermosensing properties will be optimized using a phantom of biological tissues. During the second stage the usage of novel NPs as probes for bioimaging and thermosensing will be validated and possibilities to use them simultaneously as specific drug carriers will be evaluated.
Supervisors: Alexander Vanetsev, Angela Ivask, Glen Kelp
Nanostructured cerium dioxide proved to be one of the most promising materials for many applications related to electronic and ionic mobility due to its oxygen nonstoichiometry, uniquely low temperature diffusion of oxygen anions in the lattice and high sensitivity of surface condition to external influences. The objective of this project is to systematically study “synthesis-structure-properties” relations during liquid phase synthesis of nanostructured ceria materials. The main tasks of the project include (i) synthesis of CeO2 nanoparticles by homogeneous hydrolysis of cerium (III) and cerium (IV) compounds in prehydrothermal and hydrothermal conditions, as well as in non-aqueous solvents, (ii) study of antiviral and catalytical properties of synthesized ceria particles in the form of colloids and spin-coated films, (iii) establishing the correlations between the preparation technique and activity of the material in the chosen process. It will help to reach the understanding how nanoceria works in different application systems, which centres (structural units) are responsible for its activity, how they form during synthesis and how synthetic conditions promote or inhibit their formation. This will allow for directed synthesis of nanoceria-based antiviral, catalytical and high temperature ion membrane materials with properties optimized for specific application.
Supervisors: Arvet Pedas, Kaido Lätt
Fractional (non-integer) order derivatives and questions relating to them have interested researchers for more than three centuries. Over the last couple of decades, the popularity of models that use fractional derivatives has increased exponentially in applied sciences. Since there are no universally applicable analytic methods for solving fractional differential equations, efficient numerical schemes are vital for using models with fractional differential operators. The first goal of this doctoral work is the comprehensive study of existence and regularity of solutions of fractional differential equations with non-smooth data. Based on the obtained information about regularity properties of solutions of fractional differential equations with non-smooth data, we construct and justify high-order numerical methods for such equations. We also intend to study the applicability of our ideas to the case of time-fractional differential equations.
Supervisors: Jüri Lember, Kristi Kuljus
Hidden Markov models (HMMs) is a subclass of latent variable models, where it is assumed that observed data exhibit unobserved patterns or structure that can be described by latent variables. For example, magnetic resonance tomography measurements for a human body depend on tissue classes that cannot be directly observed. HMMs are widely used for statistical modelling for example in genetics, bioinformatics and image analysis. The purpose of this project is to study different problems related to HMMs and their generalizations. The objective is to study segmentation and other inference problems for pairwise Markov models and triplet Markov models from both theoretical and practical perspective. This contains working out necessary algorithms and studying their properties and implementing the methods to real data.
Supervisors: Meelis Käärik
One of the key problems in insurance mathematics is predicting the risks and estimating the premiums based on those predictions. The problem can be divided into two parts: claim frequency and severity estimation. There are several models available to estimate actuarial risks, starting from classical approaches to brand new novel methods. With the increasing volume of information and also the speed of gathering information, the need for more dynamic and flexible methods is obvious. Yet, applying novel methods does not always guarantee better results nor more clear interpretation of the process. The goal of the thesis is to thoroughly investigate the currently available methods like generalized linear models, generalized additive models and machine learning methods, find their pros and cons, and obtain some “rules of thumb” usable for actuaries to handle risks in real life scenarios.
Supervisors: Jaak Truu, Marika Truu
Worldwide marine coastal areas and sediments are exposed to the oil spills occurring as a result of accidents or illegal practices. Among the many environmental challenges related to the accidental release of oil to sea, the management and remediation of contaminated marine sediments is perhaps the most difficult one. Bioelectrochemical systems (BES) have recently emerged, in which an electrical current serves as either electron donor or acceptor for organic pollutant bioremediation. Microbial community structure in lab-scale BES will be studied using amplicon-based, shotgun metagenomic and metatranscriptomic approaches in order to model the BES performance and increase the system treatment efficiency. Based on obtained information better predictions for intrinsic biodegradation capacity of oil fractions in sea water, sediments and coastal material can be obtained for the Baltic Sea and improved oil remediation technologies could be developed.
Supervisors: Osamu Shimmi, Tambet Tõnissoo
One important process in the development of animals is the morphogenesis of tissues and organs, which is involved in cell division, migration, deformation, adhesion and apoptosis, and is regulated by several temporally-spatially controlled developmental genes (receptors, intercellular signaling proteins, intracellular signaling proteins, regulatory proteins).
The interactions of the forming tissues through reciprocal inductive signals are crucial for morphogenesis. Although molecular biology and molecular genetics approaches revealed mechanisms underlying tissue morphogenesis during last few decades, our current knowledge is limited about how cellular mechanisms involving dynamic cell shape changes lead to lead to tissue morphogenesis.
Within the framework of this doctoral project, it is planned to use the two-layered epithelia of fruit fly (Drosophila melanogaster) pupal wing, as a 3D model to study tissue morphogenesis. We aim to address the molecular mechanisms of novel cellular structures called tunnelling nanotubes (TNTs), thin cell structures that cross the interspace between two-layered epithelia, allowing cells to communicate with each other through cell-cell contacts.
The projects will require multidisciplinary approaches including Drosophila genetics, molecular biology, state-of-the-art microscopy, live imaging and image analysis of 5D datasets (spatial 3D dimension, time dimension and wavelength dimension).
The project consists of two main objectives. First, to determine the genes involved in TNT dynamics, we will conduct a RNAi screen of up-regulated genes in the pupal wings. Our goal is a comprehensive understanding of the genes involved in TNT genesis and disassembly. Second, to understand how TNT genesis and disassembly involve structural change of microtubules we will address how key components of microtubule organizing centres (MTOCs) are utilized for these processes. This approach provides to understand how proliferative and differentiated states of the cells are regulated during tissue morphogenesis.
Taken together, these studies will lead to novel insights into how 3D morphogenesis is constructed through cell-cell communication.
Supervisors: Dr. Elin Org and Dr. Raivo Kolde
The gut microbiome is a complex and metabolically active community that directly influences various host phenotypes and in recent years our knowledge about the associations between commensal microorganism and host physiological processes has advanced rapidly. Diseases being associated with alterations in the microbiome composition range from type 2 diabetes, IBD and colorectal cancer to autism and Parkinson’s disease. Due to the seen associations, microbiome based risk scores have become a topic of interest for identifying people at risk. However, the host-gut microbiome interactions, including interactions between host genetics, metabolic activity and gut microbiome, remain largely unknown. Understanding the complex interactions and taking them into account during the risk modelling could lead to substantial increase in prediction accuracy and advancement of personalized medicine.
The goal of the current project is to understand the role of microbiome in complex diseases through investigating the interactions between human gut microbiome and various –omics data, including host genetics and metabolic activity, and to evaluate the impact of multiomics integration in risk models for complex diseases. Estonian Biobank cohort data will be used for the project, with gut microbiome data and plasma NMR profiles available for more than 2500 individuals in addition to genotyping and extensive baseline and follow-up data.
Supervisors: Tiina Tamm, Margus Leppik
Ribosomes are molecular machines responsible for protein synthesis. Protein synthesis and ribosome biogenesis are among the most energy-consuming processes in the cell. Several studies have shown that errors in both translation and ribosome biogenesis can lead to cancers or metabolic diseases. The catalytic site of the ribosome, the peptidyl transferase center (PTC), consists mainly of RNA. Several nucleotides in this region are modified. Four modified positions are evolutionary conserved from bacteria to humans. The aim of the proposed doctoral project is to understand the functional importance of rRNA modifications around the ribosomal PTC. We use Saccharomyces cerevisiae as a eukaryotic model system. The planned experiments will find out how the lack of these modifications affects cell growth and ribosome biogenesis. We also study how these modifications affect the functional activity of ribosomes - the rate of elongation, processivity and the peptidyltransferase activity.
Supervisors: Piia Post, Velle Toll
In the last decades the Baltic Sea region has warmed more quickly compared to the global average and this trend is projected to continue in the 21st century. Unfortunately, the climate change projections for the Baltic Sea region are largely diverging and uncertain. Temperature increase is universal in the climate models, but the degree of warming and associated changes in other climate parameters diverge. This project aims to understand sources of disagreements between various climate models. The dependence of regional climate change in the Baltic Sea region on changes of large-scale atmospheric dynamics is analysed. This will help to better quantify and physically understand the sources of uncertainty associated with the climate change in the Baltic Sea region. The project results serve as input for planning of climate change adaptation actions.
Supervisors: Indrek Jõgi, Peeter Paris
Present doctoral study is an important step in the development of calibration-free Laser Induced Breakdown Spectroscopy (CF-LIBS) method for the fuel (D+T) retention studies in fusion reactor walls. The concentration of radioactive tritium must be monitored due to the safety regulations. Non-contact analysis of tritium inventory in the reactor walls can be done by LIBS method which uses a powerful short laser pulse to heat and ablate small amount of investigated material and transfer the ablated material into a transient plasma state. The excited material emits light characteristic to the elements in the material. The emission spectra are then registered by spectrometer. Quantitative elemental analysis by CF- LIBS requires the fulfilment of certain plasma conditions, most notably the validity of local thermodynamic equilibrium (LTE). Present doctoral study aims to investigate the validity of LTE and applicability of CF-LIBS for fuel retention analysis in various fusion relevant materials such as tungsten, beryllium and their mixtures with oxygen, nitrogen.
Supervisors: Mihkel Pajusalu, Anu Noorma
All processes in nature are dynamic and require multi-point measurements both in space and in time to efficiently characterize them. In practice, however, the opportunities for performing such measurements are always limited and come with trade-offs, making it critical to know how, when, and where to perform the measurements. This PhD project explores the prospects of developing systems utilizing multiple agents to map and analyse such events, especially occurring in unknown environments. The goal of this project is to study the applications of multi-agent systems for mapping and characterizing dynamic events. For example, these methods can be used to plan optimal mapping manoeuvres around comets and asteroids. On Earth, these methods could be, for example, used for mapping forest fires. The project also directly feeds into Estonian participation in its first contribution into the European Space Agency’s science programme in the form of the OPIC (Optical Periscopic Imager for Comets) instrument on the Comet Interceptor mission, where the nucleus and dynamic dust jets have to be mapped using a very limited time frame and data budgets.
Supervisors: Indrek Kolka, Anna Aret
Yellow hypergiants (YHGs) are cornerstones of the evolution of massive stars forming a link between the the cool red supergiants (RSGs) and hotter pre-supernova stages called luminous blue variables (LBVs) and Wolf-Rayet (WR) stars. The YHG phase is short in the stellar evolutionary time scale and is characterized by different observable variability patterns in their brightness and spectral features. The planned doctoral thesis will investigate the possibility that selected aspects of the variability (e.g. time scales, amplitudes, correlations between physical parameters) among three representative YHGs (ρ Cas, V509 Cas and 6 Cas) depend on their actual evolutionary phase inside the common YHG epoch. Necessary observational data (spectral and photometric time series) will be gathered at Tartu Observatory and collected from the public archives (e.g. AAVSO, BRITE, TESS, Elodie, POEMS). The evolutionary ages will be estimated using available theoretical stellar evolutionary tracks (e.g. from the Geneva evolutionary database) and necessary stellar parameters based on continuously improving Gaia space telescope database.
Supervisors: Jörg Pieper, Maksym Golub
Neutron scattering methods are well-suited for direct investigations of dynamics and solution structures of proteins under nearly native conditions. In addition, in-situ experiments employing external triggers permit studies of specific functional states or of even complete functional processes in proteins. Some remarkable examples of structure-dynamics-function correlations include dynamics-mediated electron transfer in photosystems, the adaptation of protein function to high temperatures or photoprotective mechanisms in cyanobacterial light-harvesting antennae. As to the latter, the Orange Carotenoid Protein (OCP) involved in photoprotection of the cyanobacterial light-harvesting apparatus is currently a subject of an intense scientific debate. OCP is known to undergo a light-induced structural change from its inactive orange state OCPo to an active red state OCPr, which can be initiated by illumination using a laser wavelength of 450 nm at 300 K. While only the ground state crystal structure of OCP is known, the light-induced changes are still disputed. Especially, no high-resolution structures are available for the active state nor for the specific conformations that allow a binding of OCP to other protein complexes involved in photoprotection. The aim of this PhD project is to elucidate both, the structures of OCP in its active and complex-forming states as well as a potential functional importance of OCP protein dynamics as a prerequisite for undergoing large-scale structural changes. In order to achieve this goal, a number of neutron scattering techniques have to be combined: i) small angle neutron scattering with external triggers to study the structure of OCP in its different (non-crystallized) conformations, ii) neutron spectroscopy to investigate the protein dynamics, and iii) complementary molecular dynamics simulations to achieve structural models of the observed protein dynamics. We anticipate that this work will lead to a detailed understanding of the molecular mechanisms of OCP function, but also contribute to the development of complementary neutron scattering methods and their combination with theory.
Supervisors: R. Jaaniso, M. Kodu, H. Alles
The aim of the project is to integrate the gas sensors based on functionalised single-layer graphene onto CMOS chips, micro-hotplates and -lightplates. Methods to combine multiple functionalisations on a single chip will be investigated. The project is supported by Graphene Flagship.
Supervisor: Kristjan Kannike
Future space-based gravitational wave detectors such as LISA will enable us to see the cosmic phase transitions in the early Universe. In analogue to sound waves from the boiling of water, the first-order cosmic phase transitions generate gravitational waves. In the Standard Model, which describes the particles known to us, the only scalar field is the Higgs boson. The Standard Model Higgs phase transition is a cross-over and does not generate any gravitational waves. In extensions of the Standard Model with new scalars, tunnelling of the scalar field through the barrier between two minima can result in a first-order phase transition. If the scalar potential has more minima, then two phase transitions can occur one after another, and the resulting gravitational wave spectrum will have two distinct peaks. In this way, the gravitational wave signal can be put to direct correspondence with the minimum structure of the scalar potential. Especially interesting are models with classically scale-invariant potentials, where minima are generated via quantum corrections. Such models may explain the puzzling lightness of the Higgs boson and quite generally promise to produce sizeable gravitational waves signals in phase transitions. Two-step phase transitions in such models have not been studied at all. The aim of the project will be to study two-step phase transitions in multi-scalar potentials, in particular classically scale-invariant potentials.
Supervisor: Marco Kirm
The goal of research is to develop functional nanomaterials for medical applications based on the synergistic effect of X-ray irradiation and other therapeutic and diagnostic approaches. These heavy nanoparticles, delivered into the tumor tissue, create UV scintillation photons under X-ray excitation for the increase of efficiency of killing cancer cells during radiotherapy treatment. Pr3+ doped with orthophosphates emitting in UV-C will be studied to understand the interaction of different rare earth dopants upon high energy excitation and provide a detailed clarification of energy relaxation pathways subsequent to the absorption of X-rays. Wise selection of the rare earth co-dopants (e.g. Nd3+, Gd3+, Er3+, or Yb3+) in LuPO4, LaPO4 and YPO4 hosts allows to create novel materials for multifunctional imaging, diagnosis and treatment applications in medicine.
Supervisor: Stefan Groote
In this project we consider the Nambu–Jona-Lasinio (NJL) model as a useful tool to investigate the low energy behaviour of hadronic states in the framework of an effective field theory. We show that the nonlocal version of the model is renormalisable and supports the confinement of quarks within the hadronic state. Using this model, we calculate the masses of different hadronic low energy states. In addition, we look for applications of this model to gravity and other related branches of theoretical physics phenomenology.
Supervisors: Jukka Nevalainen and Elmo Tempel
According to the leading cosmological model the Universe consists mostly of dark energy while its mass is dominated by dark matter. The dark matter forms the backbone of the Cosmic Web, the filamentary network of galaxies. This model predicts that only 5% of the mass of the Universe is in the normal baryonic form (i.e. consists of protons and neutrons). In the local, low redshift Universe about half of these predicted baryons remain undetected, which introduces the serious cosmological problem of the "missing baryons". The proposed project focuses on the warm (log T (K) = 5.0-5.5) phase of the Warm-Hot Intergalactic Medium (WHIM) which is expected to constitute a significant fraction of the cosmological missing baryons. This phase is expected to reside within the cosmic filaments which from the Cosmic Web. The project aims at theoretical understanding of the whereabouts of the warm WHIM in the cosmic filaments. This is achieved via analysis of cosmological hydrodynamic simulations. This knowledge will then be used to construct predictions for the observable quantities like OVI and Broad Lyman Alpha absorption lines in the far Ultra-Violet band, and their spatial correlation with the filaments. The predictions will then be tested by comparing with the measurements of OVI and BLA absorption and the filament catalogues. A successful test would constitute a crucial step towards the solution to the missing baryons problem.
Supervisors: Mihkel Kama, Anna Aret
The study of exoplanetary systems is one of the largest and fastest-developing directions in modern astronomy. It helps us unravel Earth’s origins and the diversity of planets. Planets, disks, and stars inherit their composition from the same material, but they differ in details. To understand the origin and evolution of planets and disks, we must also study their host stars. Much of the methodological work for measuring sev eral important elements in specific planetary host stars has not yet been done, while space missions like ESA’s Ariel will require precise chemical abundances for a growing number of stars. In this project, optimal methods for exoplanet host star composition studies will be developed and applied, and data from Tartu Observatory will be exploited for exoplanetary system studies.
Supervisors: Toomas Tammaru, Erki Õunap
The lepidopteran (moths and butterflies) fauna of northern Europe is special in terms of the wealth of species-specific ecological information available, allowing for a wide array of questions to be addressed by phylogenetic comparative studies. In contrast, phylogenetic relationships of the northern European moths are still surprisingly poorly known. The proposed project aims to eliminate this shortcoming. In resolving high-level phylogenetic relationships, we will rely on nucleotide data obtained through recently developed anchored hybrid enrichment technology. Complementarily, we will use Sanger sequencing methods to resolve most lower-level (within tribes) phylogenetic relationships, using the set of genes which has become traditional for Lepidoptera. The PhD thesis will consist of separate papers presenting phylogenetic trees of different clades of Lepidoptera. Each of such papers will contribute to solving global-scale problems in the phylogeny of respective groups of moths, and will provide the necessary basis for forthcoming phylogenetic-comparative analyses.