Open positions

The overall goal of the project is to examine carry out theoretical and empirical studies on sustainability-oriented socio-technical transitions in Estonian context. The focus in on in-dept and comparative analysis of dynamics, drivers and barriers to socio-technical transitions in diverse domains from mobility to food and built environment.

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The PhD project is devoted to expanding the available synthetic portfolio of mechanochemical organic reactions, aiming to solve long-standing challenges of traditional solvent-based approaches by leveraging mechanochemical methods. The project will also focus on elucidating the driving forces in mechanochemical reactions and outlining their mechanisms, based on mechanistic experiments, kinetics measurements, reactivity trends, and stereochemical studies. The developed approaches will be applied to the preparation of bioactive compounds, pharmaceuticals, functional materials, and macrocyclic cavitands for supramolecular chemistry. Analysis of green chemistry metrics will be performed to ensure the sustainable, safe, and environmentally benign nature of the developed protocols.

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The PhD project is part of a broader research effort focused on the separation, processing, and recycling of rare earth metals. The project will exploit mechanochemical methods and metal-organic complexes to separate and valorise rare earth metals. The applicability of recycled magnets and related materials as secondary resources will also be investigated. Project will iclude development of novel membrane materials for selective separation will involve the design and synthesis of new receptor molecules (hemicucurbiturils etc) and functionalized polymers. The successful candidate will investigate the performance of these materials for the binding and separation of lanthanoids. Additionally, the PhD project will explore the reuse and valorization of magnetic materials, unlocking their full potential.

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The PhD project is part of a broader research effort focused on sensing, capturing, and separating enantiomers using hemicucurbiturils. Chiral compounds appear frequently in agricultural chemistry, cosmetics, and drug design and using hemicucurbiturils could be a cheap and green option to work with such molecules. The combinations of potential monomers, oligomers, and templates is virtually limitless; thus, optimizing the reactants for a macrocycle with specific binding properties can be a long and costly endeavor. Due to that, there is a need for accurate in silico models that could accurately screen thousands of combinations of monomers, oligomers, and templates in a relatively short time-span. To achieve this, the project will combine knowledge gained from experiments and ab initio calculations with semi-empirical and/or machine learning approaches to build models that predict binding properties of chiral hemicucurbiturils.

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Aviation has brough several benefits to modern society. It plays a central role in increasing global connectivity, allowing for faster exchange of information and technological advancements, increasing the reach of good ideas for a better society. Airborne transport is also key for humanitarian efforts that require a quick response, such as natural disasters. At the same time, this means of transportation has the highest carbon footprint per passenger-kilometer and still heavily relies on fossil fuels, setbacks that must be dealt with so the benefits of this technology can be harnessed in a long-term timeframe without compromising the environment. One of the efforts made in this direction is the development of Sustainable Aviation Fuels (SAF). Those can be produced from materials’ such as food or agricultural waste by employing microbial cell factories that can convert the carbon present in the former into molecules with high energy density like fatty acids or terpenes. However, yield and efficiency for the conversion of biowaste carbon sources into those molecules are still low, something that must be overcome to produce economically viable SAF.

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The project aims to advance the state of the art in the field of enzyme design and strain engineering. The effort is going to be directed at the engineering metabolic steps for the biosynthesis of energy-rich molecules (lipids and terpenes) that are fit to be used as raw material in aviation fuel production.

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In Estonia, there is a significant need for increased renovation activity, particularly among detached houses, in order to achieve a decarbonized building stock. Around 30% of the detached housing stock in Estonia was constructed prior to World War II. These houses were often built by the residents themselves or local craftsmen, resulting in a wide range of structural variations and varying quality. To effectively plan and implement a national renovation strategy, it is crucial to have a thorough understanding of the expected volume of renovation work and the construction materials involved. To assist and guide homeowners throughout the renovation process, it is recommended to develop the Building Renovation Passport (BRP). The BRP would provide a customized long-term renovation and maintenance plan for each building. Since the BRP preparation is a big job, it requires a lot of input and work. Since there are not enough human resources for this, it is necessary to implement artificial intelligence, expert systems, knowledge systems and configuration systems. Implementation of these systems requires knowledge-based input data, analyses, and results.

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Plastic pyrolysis, a process converting plastic waste into valuable chemicals and fuels, can generate persistent organic pollutants (POPs) such as dioxins, furans, and PAHs. These toxic and long-lasting chemicals pose significant environmental and health risks. Proposed research topic aims to identify and reduce the formation of POPs during pyrolysis by optimizing conditions and developing degradation methods. Advancing this knowledge is crucial for making plastic pyrolysis a safer and more sustainable waste management solution, protecting both environmental and public health

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Ventilatsiooni õhujaotuse efektiivsust tuleb rakendada mistahes ventilatsiooni kriteeriumi järgi projekteeritavas ventilatsioonisüsteemis, et tagada hingamistsoonis vajalik kogus puhast õhku. Tavapäraselt on vaikimisi oletatud seguneva õhujaotuse kasutamist, mille puhul kõikides ruumi punktides ja väljatõmbeõhus on sama saasteainete kontsentratsioon. Selline olukord saavutatakse paljude mitte-eluhoonete lahendustega eeldades, et saasteainete allikad – ruumis viibivad inimesed, on enamvähem ühtlaselt jaotunud ja moodustavad hajutatud saasteallika. Nakatumisriski põhine ventilatsiooni kriteerium lähtub tavapärasest olukorrast erinevalt punktsaasteallikast, millel võivad olla ruumis mitmed, tavaliselt töö- või istekohti järgivad asukohad. Kuna nn. segunev õhujaotus ei pruugi eriti suuremates ruumides punktsaasteallika korral töötada seguneva õhujaotusena, siis on suur vajadus õhujaotuse efektiivsust hindava metoodika arendamisele ning tüüpiliste väärtuste leidmisele erinevatele lahendustele, mida projekteerimises oleks võimalik kasutada. Samuti on vajadus arendada ja uurida õhujaotuslahendusi, mis suudaksid kõrge efektiivsusega töötada nii hajutatud kui punktsaasteallikate korral.

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Kliima soojenemise tõttu on Eestis uuendatud energiaarvutuse baasaastat ning samaaegselt muudetakse rangemaks suvise ülekuumenemise vältimise nõuet, mille järgi alates 1.3.2025 ei tohi elamutes kolme suvekuu temperatuur ületada 26 kraadi rohkem kui 150 kraadtunni jooksul. Lisaks on loodud tulevikukliimafailid vabatahtlikuks kasutuseks. Muudatused tekitavad varasemast oluliselt suurema vajaduse korterite jahutuslahendustele, sest eeldatavasti tuleb enamus päiksepoolseid kortereid varustada mingit tüüpi aktiivse jahutuslahendusega.

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Countries with differing modes of governance – such as more federalised or unitary countries – have experimented with differing modes of e-governance – such as IT ministries, executive commissions, or Chief Information Officer (CIO) positions – to develop and help implement their digital transformation strategies. However, this mix of contexts and methods has yielded varying degrees of success; governments who modelled their own digital transformation governance following best-practice examples from other countries find that the same model does not function for them, and vice versa. This study seeks to explore the interdependency of modes of political governance and modes of e-governance and their performance in successfully implementing digital transformation initiatives. The study will provide valuable insights and actionable recommendations for creating e-governance models that suit particular countries contexts, thereby creating more equitable, inclusive, and prosperous societies.

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This interdisciplinary experimental project lies on the intersections between photophysics, physical chemistry, nonlinear- and quantum optics and studies multi-photon optical absorption and -emission by organic and metalloorganic fluorophores with the aim of improved understanding of the fundamental relation between chromophore’s structure and the efficiency of multiphoton processes with the ultimate objective of elucidating the nature of light particles - photons. The experiments will address key physical aspects such as ground-state symmetry, quantum state degeneracy, interaction between chromophore’s electronic- and vibrational motion, nonlinear saturation of absorption as well as two-photon stimulated emission. The experiments will be conducted using state-of-the-art femtosecond nonlinear-optical spectroscopy instrumentation developed at the National Institute for Chemical Physics and Biophysics. These investigations will facilitate novel and augmented applications in biological microscopy, 3D nano-printing, photocatalysis and quantum information. Qualifications for this position include master’s level physics, physical chemistry or equivalent. Candidates should exhibit enthusiasm towards practical laboratory work including femtosecond lasers. This interdisciplinary experimental project lies on the intersections between photophysics, physical chemistry, nonlinear- and quantum optics and studies multi-photon optical absorption and -emission by organic and metalloorganic fluorophores with the aim of improved understanding of the fundamental relation between chromophore’s structure and the efficiency of multiphoton processes with the ultimate objective of elucidating the nature of light particles - photons. The experiments will address key physical aspects such as ground-state symmetry, quantum state degeneracy, interaction between chromophore’s electronic- and vibrational motion, nonlinear saturation of absorption as well as two-photon stimulated emission. The experiments will be conducted using state-of-the-art femtosecond nonlinear-optical spectroscopy instrumentation developed at the National Institute for Chemical Physics and Biophysics. These investigations will facilitate novel and augmented applications in biological microscopy, 3D nano-printing, photocatalysis and quantum information. Qualifications for this position include master’s level physics, physical chemistry or equivalent. Candidates should exhibit enthusiasm towards practical laboratory work including femtosecond lasers.

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This PhD project focuses on creating a real-time and wide-area measurement based understanding on power system stability in converter based generation dominated power systems. The research aims to develop new methods for determining power system operating conditions and limitations when generation mix and locations are changing. The results of the project will be validated in real power system using available wide-area monitoring system.

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The task of the PhD project is to research the parameters that drive the quality of electrospun nanofiber layers, and devise the methodology of multiparameter monitoring and control of the electrospinning process. The ultimate goal is to demonstrate the viability of the developed approach in multilayer electrospinning. The project is part of Estonian Research Council project PSG897 “Cogni-E-spin: Cognitronic Electrospinning System for Automated Quality Control of Nanofiber Product”, which aims to create the technologies for scalable inline quality control of electrospun nanofiber meshes. The project is endorsed by industrial partners in Estonia and Hungary.

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This PhD project focuses on creating a real-time co-simulation platform to optimize the scheduling of energy flexibility in nearly Zero Energy Districts (nZEDs). It integrates various models and advanced forecasting algorithms to manage flexibility resources accurately. The research aims to develop new methods for aggregating and forecasting energy flexibility using deep learning, and to optimize resource scheduling to minimize costs while adhering to grid constraints. The project will validate its methods to ensure practical and reliable application in smart grids, enhancing their efficiency and sustainability.

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The goal of the planned research is to develop and test experimental neurosymbolic systems for specific application areas. The systems should be able to understand rules and facts given in natural language in the application area context. It is expected that they should use a large language model (LLM) for creating symbolic knowledge which is usable -- for example -- for database queries, API calls, search algorithms, reasoning engines and external tools, and based on these, give answers to questions posed in natural language, so that the answers are explained, verifiable and have confidence estimations attached. The main technologies to be explored and combined are different types of machine learning, existing LLMs, databases, API-s and specialized automated reasoners.

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Nowcasting is an important and integral part of operational weather services spanning weather monitoring, road weather warnings, aviation applications etc. The explosive growth of artificial intelligence applications in recent years opens new opportunities for integrating multiple data sources and development of predictive systems for nowcasting operations. The aim of the project is to develop and test deep learning methods to enhance quality of nowcasting predictions over Estonia. The primary focus of the project is on enhancing the quantitative precipitation forecast skill based on the application of remote sensing data which will be complemented with data from traditional ground observations and numerical weather prediction models.

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The aim of this project is to understand the role that mechanical and thermal effects play in signal propagation in nerve axons. The proposed study is mostly related to mathematical modelling and numerical experiments.

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We invite applications for a fully funded PhD position focused on the socio-economic cost-benefit analysis of the European Commission's "Renovation Wave" initiative. This PhD project aims to analyze the economic, social, and environmental impacts of large-scale building renovations targeted at enhancing energy efficiency, particularly within urban districts. The successful candidate will contribute to developing robust analytical frameworks to assess the costs and benefits of renovation projects, thereby informing policy and investment decisions.

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Electrical machines are the workhorses of modern industry. Yet, they are prone to different faults, meaning the condition monitoring and diagnostics of the electrical machines is a highly important task in order to ensure prolonged lifetime of the systems, continuous undisturbed work and minimizing economical losses. The electrical systems are becoming ever more complex, leading to the necessity of novel and more advanced approaches of diagnostics and condition monitoring. The overall goal of the project is to examine the opportunities and pitfalls involved predicting the faults in machines, leading to dealing with the roots of faults at early stages rather than eliminating the results of failures. The project focuses on selection of suitable diagnostic methods, modelling and simulation of faults, diagnostic and prognostic algorithms, extensive laboratory testing of fault case scenarios and validating the methodology through analysis of all the mentioned steps.

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Under global warming, exacerbated weather and ocean conditions are expected. Climate warming has resulted in a steady increase of air temperature over the world ocean. Ocean heat content of the Baltic Sea has responded to the global warming trend. Main aim of the research is to prepare seasonal forecasts of the ocean heat content in the Baltic Sea using transformer neural network models. The data used for the study consists of model reanalysis and seasonal forecast data from the Copernicus Marine Service database, ERA5 climate reanalysis database and ECMWF seasonal forecasting system’s SEAS5 data.

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The main objective of the research is to develop the intelligent workplace of mechanical product assembling with AI functionality, for flexible manufacturing in the field of mechanical engineering, electronics, furniture, and food industries.

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Climate warming has resulted in a steady increase of air temperature over the world ocean. Ocean mixed layer directly interacts with the atmosphere. The Baltic Sea ecosystem is strongly impacted by changes in the mixed layer thickness because of primary production is mainly taking place there. Main aim of the research is to prepare seasonal forecasts of the mixed layer depth and cold intermediate layer volume in the Baltic Sea using transformer neural network models. The data used for the study consists of model reanalysis and seasonal forecast data from the Copernicus Marine Service database, ERA5 climate reanalysis database and ECMWF seasonal forecasting system’s SEAS5 data.

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The NMR group in the National Institute of Chemical Physics and Biophysics (KBFI) in Tallinn, Estonia, has an opening for a PhD position for developing applications for complex (bio)mixtures analysis by parahydrogen hyperpolarization.

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This doctoral thesis investigates how Grid Enhancement Technologies (GETs) such as Dynamic Line Rating (DLR), Flexible AC Transmission Systems (FACTS), Advanced Conductors, Grid Energy Storage, and Advanced Distribution Management Systems (ADMS) affect power system stability. By utilizing Phasor Measurement Units (PMUs), Wide Area Monitoring Systems (WAMSs), and Real-Time Digital Simulators (RTDS), the research provides a comprehensive analysis of the improvements in grid capacity, efficiency, reliability, and safety. This position researches the effect of implementing GETs on system stability in modernizing power infrastructure and ensuring a resilient energy future.

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The doctoral project focuses on optimizing resource efficiency in the mining of construction minerals in Estonia, using the principles of the circular economy and sustainable mining practices. The goal is to develop innovative technologies and strategies that promote sustainable mining, recycling of construction minerals, and effective handling of mineral waste. By integrating the principles of the circular economy, the aim is to reduce environmental and social impacts, address regional shortages, and contribute to the sustainable development of Estonia's mining sector through the development of alternative mining technologies.

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Unconventional superconductivity is an active field of condensed matter research, where the theoretical models can be experimentally differentiated by the predictions they make for the symmetries of the superconducting order parameter. Interesting to us chiral superconductors possess non-trivial topological properties resulting in superconducting order parameters that may break time-reversal symmetry, and that we will experimentally detect by measuring the polar Kerr angle.

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The global demand for permanent magnet materials is increasing fast. Neodymium-iron-boron (NdFeB) magnets are essential for the realization of the targeted e-mobility and green energy milestones by enabling the construction of the most compact and powerful electrical machines. The project will develop recycled NdFeB raw materials and characterize them with new and competing products on the market. The overall goal of the project is to develop a methodological expertise in recycling permanent magnets and includes both theoretical and experimental work. The project focuses on optimization in terms of magnetic parameters by both bulk (magnetometry, VSM) and microscopic (NMR-NQR) magnetism-based methods with the goal of maximizing the recycled NdFeB magnet power density. The project is a close collaboration of labs at KBFI/NICPB, TalTech, and Tartu University with practical guidance of the new Narva magnet factory by Neo Performance Materials Inc (NEO) utilizing both commercial and in-house produced NdFeB samples.

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The overall goal of the project is to research and develop a new methodology for assessing the dynamic response that occurs after the activation of demand-side energy flexibility. The activation of energy flexibility, especially on an aggregated level, causes a deviation in the usual demand profile. This intentional deviation is sometimes necessary for proper grid management. However, after the activation of energy flexibility, the system needs to return to its normal state. During this process, dynamic responses such as the rebound and overshoot effects occur that need to be investigated.

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The project focuses on providing a framework for a safe-and-sustainable-by-design (SSbD) approach for the metal-phenolic network (MPN)-coated nanoparticles (NPs) (MPN nanocomposites) in metal recycling from wastewater. The aim is to conduct a systematic hazard assessment of a battery of MPN nanocomposites with good metal adsorbing properties (following 3R principles, i.e., without using vertebrate animals), and, based on these data, identify characteristics of MPN nanocomposites for safe and effective applications. The project will contribute to a modern and environmentally responsible circular economy.

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Glauconitic sandstones, with K- and Fe-rich complex clay mineral glauconite, are widespread, albeit heterogeneous, lithologies. The PhD project focuses on finding novel industrial usage, such as alternative green fertilisers, for those materials based on an interdisciplinary study combining applied mineralogy, geochemistry, and mineral processing. The study involves characterising mineral features, crystallochemistry and textural properties of glauconitic sandstones with various routine and state-of-the-art technologies. The other part of the project is based on experiments on the mechanical activation of glauconite. The main targets will be Ordovician glauconitic sandstones from Estonia. The study aligns with the zero-waste mining concept, as in Estonia, the glauconitic sandstone is a potential waste rock of phosphorite mining. The project is jointly supervised by the TalTech Department of Geology and the Geological Survey of Estonia. It will be carried out under the research project TEMTA100.

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Consumption of dietary fibres (DF) should be increased to comply with the dietary guidelines and fuel various beneficial metabolites produced by the gut microbiota such as short chain fatty acids (SCFA). Microbiota also produces gases that, in sensitive persons including irritable bowel syndrome patients (IBS), trigger disturbing and at worst disabling gastrointestinal (GI) symptoms, leading to the avoidance of fibre-rich foods. The amounts and formation patterns of the gases are highly individual and interrelated to GI parameters such as gut pH and transit rate. Current PhD position is a part of the project, which aims to develop and validate a concept to match DF types to gut microbiome subtypes for optimal metabolic output. The project identifies major DF-metabolising microbiome types prevalent in Nordic countries using existing metagenomic, dietary and GI symptoms data, considering also endogenous glycans. This doctoral position is planned for in vitro studies and microbiome-tailored food development at Tallinn University of Technology, Estonia. Cultivation experiments include screening tests as well as deeper metabolic studies on how gases are formed by targeted microbial consortia from different dietary fibres. the most potential fibres will be selected for microbiome-tailored food development. This study is interdisciplinary and will integrate researchers from different fields - chemistry, biotechnology, molecular biology, bioinformatics and food science. We collaborate with University of Helsinki and Örebro University.

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Equivalent single layer (ESL) approach whereby the stiffened panel is replaced with a single plate is an efficient means to model and perform non-linear analyzes of large or composite structures [1]. The basis for the approach are the unit cell simulations, which describe the underlying structural behavior and need to be run beforehand to enable homogenization. This is also the biggest bottleneck of the methodology. Therefore, the objective of this work is developing a surrogate model that could replace the unit cell analysis using data-driven and machine learning methods. As the ESL model would be used for buckling response, vibration response, accidental and ultimate limit state analysis, the developed surrogate model could potentially cover all these loading scenarios but can also be limited to only one of those scenarios.

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In this position you need to develop a numerical approach for coupled ice-structure interaction assessment for ship and offshore structures.

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Additive manufacturing technologies support the sustainable manufacturing goals of EU by facilitating the production of next-generation electrical machines (EM) and simplifying the involved logistics. The main advantage of AM systems is their capacity to cost-effectively produce parts with complex topologies that have previously been out of reach of traditional methods. Current laser powder bed fusion additive manufacturing facilities in Taltech are capable of producing high quality metallic parts with useful mechanical, magnetic and electric properties. What is lacking is the multi-material option, e.g. the possibility of adding insulating layers on specific surfaces of a 3D-printed part. The project will explore the possibility applying such coatings on 3D-printed soft magnetic stator cores in order to suppress the induced eddy currents. The work on this project will include both theoretical and practical work, including operating a laser powder bed fusion (L-PBF) system to 3D-print 6.5% silicon steel and the study of different processes (such as dipping, electroplating, PVD or CVD coating methods) to enhance its performance characteristics.

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This research will target the development of zero emission buildings, focusing on their energy management methods and optimization of their costs and performance. The cross-disciplinary work will address building’s energy consumption, generation and storage from various perspectives, that intensify the use of on-site generated renewable energy and energy storage at the building and community levels.

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In this position you need to develop a digital twin of a ship operating in ice infested waters. You will use existing numerical model, which you will improve and validate using experimental ice-structure interaction data gathered on a real ship. The experimental data involves ship motion and performance data, strain gage data, and ice conditions information (visual observation together with camera-based identification system). Therefore, you must come up with a working solution that can integrate observational measured data (with different sampling frequencies) into a simulation environment. At first, this simulation environment will be Abaqus. This integration is referred to as digital twin framework. As part of this framework development, the research should quantify the uncertainties and relevant simplifications, which would enable fast model development (real-to-digital) and accurate ice-structure interaction simulations. There are two goals in this work. Short term goal is to develop realistic structural analysis model, while long term goals is to reach a reduced order model (ROM) that is deployable in a DSS (decision support system).

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Clean indoor air is an essential determinant of healthy life and people’s well-being. Refractory volatile organic compounds and odors emitted from buildings, construction materials and indoor equipment comprise a broad range of health issues. Air purification methods integrated with ventilation systems allow to reduce indoor air pollution. Thus, the overall goal of the present project is to examine the combination of pulsed corona discharge (PCD) and photocatalytic oxidation (PCO) aimed to chemical degradation of airborne pollutants. Several important questions need to be addressed on the course of proposed research: What is the role of temperature in the formation of oxidative species in plasma environment? How to improve the degradation of ozone in PCO? How successful could be the application of solar energy to induce PCO process?

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TalTech Laboratory of Wood Technology is offering PhD position to well-qualified individual to work on a PhD research project, which aim is to create highly reproducible model surfaces through the controlled technological processing. Model surface will be used to study systematically the interactions of wood and adhesive in order to understand the mechanisms involved. In any adhesion related study the highly reproducible surface is a key for successful research, however it is challenging task, especially in case of natural polymer materials. Subsequently model surface will facilitate the development of new test methods and implementation of new environmentally friendly adhesives. TalTech has excellent facilities and staff to accomplishing this research. TalTech has a wide range of analytical techniques for surface imaging and has a laboratory scale veneer peeling and veneer-based prducts production line. This laboratoty-scale line allows controlling processing parameters and create high quality veneers for engineered veneer products. The individual assigned to this position will be enrolled to the doctoral study program “Engineering Sciences” in speciality “Chemical-, Materials-, and Energy Technology” and employment as early-stage researcher by the Department of Materials and Environmental Technology.

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PhD position is opened for characterization of lignocellulosic substrates for mycelium applications and for development of fire-retardant treatment methods for mycelium material. Mycelium grows the skin that covers the lignocellulosic substrate, thereby inhibiting any fire retardant pre-treatment, thus, treatment with fire retardants after the mycelium material has grown is more effective.

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Ph.D. student will work on innovative solutions to further advance high energy conversion efficiency intrinsic to DC microgrids. This Ph.D. work includes but is not limited to developing novel topologies and control approaches based on the partial power conversion principle and their real-world application in DC microgrids for renewable energy integration, storage, power flow control, etc.

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Ph.D. student will work on non-isolated DC-DC converter topologies in applications with harsh environments characterized by unique operating conditions, like high magnetic field, high radiation, or high/low operating ambient temperature. These applications are limited by the availability of suitable components and, thus, require special design approaches that can ensure their high reliability, power density, and efficiency. This Ph.D. work includes but is not limited to developing novel topologies, control methods, and implementation approaches for non-isolated DC-DC converters.

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Ph.D. student will work on DC microgrids represented as cyber-physical systems and their security and conditions monitoring employing physical sensing and cybersecurity domain methods. This approach is expected to enhance the resiliency of DC microgrids to physical faults and attacks, as well as cyber-attacks. The cyber-physical domain can provide more complete information for distinguishing faults from attacks and isolating them, ultimately improving the resilience of DC microgrids.

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The Ordovician Period was characterised globally by a cooling climate and a significant diversification of life, punctuated by the end-Ordovician mass extinction. This project aims to document and understand how biodiversity trends are linked to climate change and environmental perturbations on the Baltica palaeocontinent. Two model groups of organisms will be studied: planktic chitinozoans and benthic jawed polychaetes (represented by scolecodonts). An integrated palaeontological database will be created and used to quantitatively analyse the distribution of faunas in time and space, distinguishing climatic effects and environmental changes from evolutionary and paleobiogeographic factors.

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Under global warming, exacerbated weather and ocean conditions are expected. The importance of sub-seasonal and seasonal weather predictions is continuously increasing worldwide. Climate warming has resulted in a steady increase of air temperature globally. Ocean heat content of the Baltic Sea has responded to the global warming trend. Main aim of the research is to prepare and validate seasonal forecasts for the Baltic Sea region by dynamical downscaling of global seasonal predictions. The data used for the study consists of ERA5 climate reanalysis database and ECMWF seasonal forecasting system’s SEAS5 data.

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The PhD project uses postglacial sedimentary pollen and NPP analysis to reconstruct terrestrial landcover, land use and regional climate patterns in North Eastern Europe. The PhD topic focuses on the evolution of peoples and cultures on the eastern coast of the Baltic Sea since their first arrival following the retreating continental ice margin. The student will work in an exceptional level group specialised on palaeoecological studies. Furthermore, through multidisciplinary cooperation the project offers possibilities to combine pollen-based datasets with other palaeoecological proxies, eDNA and sedaDNA analysis results and archaeological knowledge base. The novel datasets will be analyzed in a temporal, spatial and ecological context using state-of-art tecniques allowing to map the interactions of human cultures and populations with abiotic (climate) and biotic (flora, fauna) factors.

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Blending of machine learning approaches, onboard measurements, and numerical simulation data present an opportunity to develop predictive models for vessel motion forecasting. Such predictive, or surrogate models, embedded in the decision support systems (DSS) of manned and/or unmanned vessels will be an integral part of next generation control or compensation systems as well as digital twin based applications. Ships and other marine vessels have already in most cases access to ship motions (heave, pitch, roll, accelerations etc). Based on vessel trajectory the control system has access to wave forecast and hindcast data which could be further combined with on-board wave identification systems. Combination of these data sources offers an opportunity to optimize vessels performance in terms of safety and efficiency. The goal of this work is to develop predictive data-driven model for ship motions using numerical simulations and test its applicability in real-world conditions.

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The overall goal of the project is the development of green fractionation technologies for the extraction, purification and depolymerization of lignin from different kinds of biomass and its further chemical valorization. By harnessing the potential of lignin as renewable resource this project also contributes to the development of various new lignin-based functional materials (carbon fibers, adhesives etc.) that can effectively replace fossil-based counterparts—a goal that aligns with the principles of responsible consumption and production outlined in the United Nations SDG. The project also focuses on the development of reliable analytical methods for the qualitative and quantitative assessment of feedstock, fractionation products and new materials.

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PhD position goal is to elaborate principles and strategies related to protection of electromagnetic spectrum from wideband interference, related to the perspective technologies to be deployed in partial power DC/DC high-efficiency power electronic converters technologies and systems composed on these units. Research questions targeted are related to 1) appropriate measurement methods and procedures to assess the electromagnetic spurious emissions levels from operating converters 2) basic phenomena responsible for the potential electromagnetic emissions in partial power DC/DC converter designs 3) potential strategies of the long-term monitoring of electromagnetic disturbance levels and propagation in view of extended wearout and aging of the components on the power electronic energy conversion units.

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The Department of Energy Technology at Tallinn University of Technology is seeking a highly motivated and dedicated PhD candidate to join our research team. The successful applicant will work on a cutting-edge project focused on Chemical Looping Gasification (CLG) Technology of Biomass and Oil Shale

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The overall goal of the project is to investigate and develop a chemical looping gasification (CLG) process as a novel CO2 capture technology for the thermochemical valorization of oil shale and biomass (including wood waste) into raw materials for the chemical industry and building materials with simultaneous energy production. More specifically, this project will explore ways to maximize the value of oil shale and biomass in a waste-free and climate-neutral way. The aim of the project is to produce gaseous H2 rich or directly high purity H2 products and ash suitable for the cement industry. The characteristic of the CLG process is the CO2 neutral H2 rich syngas production at low specific costs (no air separation unit required) and high efficiency. The use of oil shale as a feedstock can allow an additional process stream to be achieved in the form of CO2 neutral ash used as a cement additive. CLG can be capable of producing carbon-negative hydrogen when carbon-neutral biomass is used as feedstock.

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This topic aims to contribute to the development of innovative hydrogen storage materials as sustainable energy sources to help achieve climate neutrality in both the energy and transport sectors. The project uses renewable natural resources (biomass) and solid mineral waste as raw materials to develop a sorbent material for hydrogen storage.

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In this position, you develop an active motion control systems for (unmanned) surface vessels and structures. Aim of the control systems is to actively damp the angular motions of the vessel based on the identified wave/environmental conditions and the feedback from ship’s response.

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Ph.D. student will work on innovative solutions for particle accelerator applications, including but not limited to power supply balancing for high-power klystrons, energy storage integration in power supply system of accelerator magnets, feasibility studies of muon collider, etc.

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The ambition of the Laboratory for Thin Film Energy Materials at TalTech is to create and provide the next generation of thin film photovoltaic technology capable of powering IPV/IoT devices and adaptable for integration in solar windows and related BIPV products. This PhD research topic explores the effect of Ag concentration in the precursor solution, from the doping side (Ag-Sb2S3) to the ternary compound (AgSbS2) formation, to the formed thin film properties. Sb2S3 is an emerging inorganic PV material that have drawn much interest in recent years due to its excellent stability, suitable bandgap (Eg=1.7 eV), relatively high absorption coefficient (ca104 cm-1 at 450 nm), earth abundance, environmentally benign characteristics and low-cost. Due to its unique properties Sb2S3 could be applied in semi-transparent, tandem, and indoor solar cells. This research topic foresees characterisation of material and device properties at TalTech and at research group collaboration partners (e.g., Liverpool University, or Czech Technical University or University of Verona or Helmholtz Centrum Berlin). We offer an opportunity to be part of the COST action CA21148 - Research and International Networking on Emerging Inorganic Chalcogenides for Photovoltaics, RENEW-PV, https://renewpv.eu/

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In this position, PhD candidate is expected to develop sea/ocean environment perception, navigation methods for future autonomous surface vessels. Using state-of-the-art deep learning models together with various environment sensing methods, the candidate is required to devise novel solutions that can help interpret the environment for safter ship navigation. Detection and recognition of diverse moving objects (ships, yachts, sailboats, etc), stationary objects (buoy) and characterization of different sea states (ice infested, breaking waves, etc) are the vital constituents of situational awareness for the ships which need to be addressed in this research.

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The overall goal of the project is to research and develop a new methodology for low voltage distribution grid planning considering the impact of power generation and consumption pattern changes on coincidence factor. The long-term strategy of the European Union is aiming at climate neutrality to be achieved by 2050 poses serious challenges to all branches of the economy, incl. the energy sector. It is expected that 50% of the supplementary renewable energy sources will be connected to the distribution network, of which 25...50% are direct current solutions added to the network through power electronics. The increasing sporadic nature of power generation and consumption and frequent changes in power flows constitute a challenge to the electric power system in several aspects: quality of electricity, supply chain security, reliability of network components, network losses as well as unpredictable end-user price fluctuations etc.

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The global demand for permanent magnet materials is increasing. NdFeB neodymium magnets are essential for the realization of the targeted e-mobility and green energy milestones by enabling the construction of the most compact and powerful electrical machines. The project will combine recycled NdFeB raw materials with the production freedom of additive manufacturing (AM) technologies to realize innovative topology optimized rotating electrical machines. The overall goal of the project is to develop a methodological expertise in 3D-printing permanent magnets and includes both theoretical and practical work. The project focuses on the optimization of the pre-printing, printing and post-printing processing parameters when working with two distinct AM technologies: laser powder bed fusion (L-PBF) and extrusion (FDM) based methods with the goal of maximizing the AM NdFeB magnet power density. The utilized raw materials will include both commercial and in-house produced NdFeB powder from recycled sources

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Additive manufacturing (AM) is evolving rapidly and it has been seen in the world as important step towards the next industrial revolution, being one of the key features of decentralization the production of high complex structures. The flexibility of the technology also allows the production of electromechanical components and even electrical machines, which can have significantly better properties compared to conventional technologies. However, it is an innovative industry and lacks the knowledge and skills to take advantage of this technology. AM opens the possibility to introduce unconventional three-dimensional complex structures and constructional elements, which allow the production of electrical machines with novel magnetic circuit design solution. Yet there is not existing well developed design methodology how to take these advantages into account. The project focus on the development of the design methodology for switched reluctance machine considering the AM advantages. The objective of the project is selection and definition of innovative electrical machine design and working out the calculation methodology most beneficial for using AM technology. Practical part of the work will be printing the developed machine and validating the developed methodology in the lab.

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Problem formulation: EU goals regarding to green energy and emissions (raise of photovoltaic and wind generation); No combustion engines from 2030 (raise of Electrical Vehicles); Grid contingency (problem with the transfer capacity and voltage). Optimization tasks: power flow, losses, voltage, costs (for grid operators and prosumers). Solutions and methods: BESS, control strategies for EV and RES, reactive power control. Tools: Matlab (optimization) and DIgSILENT (grid simulations). Implementation area: Distribution grid, point of common coupling between Distribution and Transmission systems.

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The overall objective of the PhD study is to develop sensing devices by implementing Molecularly Imprinted Polymers (MIPs) as robust, low-cost biomimetic receptors for minimally invasive and noninvasive measurement of molecular biomarkers of wellbeing states, e.g. stress. The study addresses the urgent need for wearable and point of care (PoC) devices for noninvasive monitoring of stress-related disorders. It aims to overcome the limitations of current testing devices, particularly their restricted capability to analyze complex samples and the use of labile biological receptors as recognition elements. The resulting sensing devices are expected to provide an affordable and easy-to-use analytical tool capable of accurately analyzing biological fluids, such as sweat, in a noninvasive manner.

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The overall objective of the PhD study is to develop next-generation sensor arrays by implementing Molecularly Imprinted Polymers (MIPs) as robust, low-cost biomimetic receptors for multiplex and/or simultaneous detection of targets that are of significant interest to medical diagnostics and environmental monitoring. The study addresses overcoming the limitations of current biosensors and point-of-care testing devices, particularly regarding their restricted capability to analyze complex samples and the use of biological receptors as recognition elements. The resulting sensor arrays are expected to provide an affordable and easy-to-use analytical tool capable of accurately analyzing complex environments, such as environmental water or biological fluids, in a multiplexed manner.

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Photovoltaic (PV) is recognized as one of the main renewable energy solutions for fulfilling the targets defined by the EU Energy Roadmap 2050 and the SET Plan. To meet the rapidly increasing demand for solar power generation capacity, it is essential to develop technologies that are more ecologically sustainable, more efficient, and versatile enough for a wide range of applications. The main goal of the research group at the Photovoltaic Materials Laboratory of Tallinn University of Technology is to achieve a significant breakthrough in the technology of flexible solar panels based on kesterite (CZTSSe) materials. CZTSSe is one of the most promising light absorber material candidates for potential use in lower-cost thin-film solar cells. Recently, there have been significant breakthroughs in thin film kesterite technology resulting in several efficiency records in a very short time using a simple solution-based process. Although, there are still many challenges to reach closer to theoretical maximum efficiencies. The overall goal of the PhD project is to further develop and optimize non-toxic, earth-abundant kesterite absorbers for efficient thin-film solar cells using a solution-based technique.

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The indoor photovoltaic (IPV) has great market potential as the demand for constantly available energy sources grows, especially for small electronic devices and Internet of Things (IoT) devices. The project focuses on developing flexible kesterite-based monograin layer solar cells specifically designed for indoor photovoltaic applications. The aim is to customize the optical and electronic properties of the absorber material and the device architecture so that they better match indoor light sources such as cold and warm LEDs.

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The overall long-term goal of the project is to change the perspective of the oil shale industry by focusing on the transformation of organic materials present in different waste streams of the industry into valuable chemicals. This will eventually lead to the development of an environmentally friendly chemical industry. To achieve this goal, methods for the sustainable use of by-products from the oil shale valorisation processes need to be either developed or the existing methods need to be optimized for the specific resources available. The objective is to further develop innovative uses for raw materials that create added value and significantly enhance the efficiency of local resource utilization, which aligns with the general green transition strategies. More specifically, the focus of this PhD project is set on the separation and implementation of the by-products formed during the valorisation of organic material from waste rock piles. The project addresses the following research questions: What are the potential streams to be used as sources for valorisation? What are the optimal conditions for the specific resources? What type of products can be obtained and what is their market potential? What are the potential industrial uses for the obtained materials (for example, can they be used in the building industry, etc.)?

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The Intergovernmental Panel on Climate Change (IPCC) has noted with "very high confidence" that global warming reaching 1.5°C in the near-term will present multiple risks to ecosystems and humans, and that immediate actions to limit global warming can substantially reduce future losses and damage. In parallel to climate change, we currently face global energy and resources crises necessitating the development of science for new, sustainable production technologies and approaches. The construction industry is at the heart of these issues. In the European Union, it accounts for approximately half of all raw materials used, one third of all waste generated and 40% of final energy consumption occurs in buildings. The Council of the European Union has specifically identified the need for Circular Economy in the Construction Sector and the importance of research in bringing about this transition. However, the planned transition to circular production and operation approaches in the construction sector also carries with it considerable (economic, social, environmental and political) risks which this research will systematically explore.

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We are looking for a highly motivated and self-driven PhD student/Early-stage researcher to investigate the state-of-art technologies in the realm of cold thermal energy storage (cold TES) systems. As a doctoral researcher, you will contribute to the development of physics-based/machine learning models for enhanced performance, efficiency, and reliability of Cold TES in the context of district cooling applications. The prospective candidate will be part of TalTech's research group of Smart District Heating Systems (led by Prof. Anna Volkova), focused on conducting industry-relevant research, writing top-tier scientific publications, and participating in funded projects.

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Offshore wind farms radiate underwater sound into the marine environment. Marine life can perceive and be disturbed by underwater anthropogenic noise and water particle vibration. To understand the impact of wind turbine noises on marine life the sound pressure as well as the particle motion field should be measured and analyzed. Establishing the relationship between these two fields is the key question to predict particle motion values of the various sources of anthropogenic noise. This study is carried out jointly with marine biologists from the University of Tartu.

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This doctoral research proposal aims to study the economic impact of various policy interventions and regime changes in quasi-experimental or regime-varying settings that among others arise from global crises such as COVID-19 pandemics, geopolitical tensions and wars, and climate disasters. The proposal seeks to apply and compare quantitative evaluation methods for generating better understanding on how to consistently estimate intervention effects in varying research contexts paying mostly attention to implications at the level of microeconomic units, including firms, households, or individuals. In doing so, the research identifies the strengths and weaknesses in evaluation approaches and methods in different contexts and provides stronger foundations for evidence-based policy making.

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This research will target the development of intelligent energy routers interlinking AC grid with DC buildings and associated control methods and algorithms needed to provide services to the distribution grid. Also, it will consider the extension of the DC distribution to energy-efficient smart districts towards new energy services for communities.

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TalTech’s School of Business and Governance offers ambitious individuals the opportunity to join the Economics specialization of its PhD programme. The PhD programme is offering 1 open topic PhD position in the research area of Economics & Finance. The prospective PhD students are requested to propose their own specific research project that contributes to the School’s position as a leading Business School in the region and beyond. The proposed research project should align with the research activities of one of the research groups active at the Department of Economics & Finance at the School of Business and Governance – see more here: https://taltech.ee/en/sbg/research-groups

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The project focuses on developing and applying novel labelling techniques for different droplet-based high-throughput assays. This includes employing a vast array of techniques such as hands-on construction and manipulation of microfluidic platforms, generation of small (pico- and nanoliter sized) water-in-oil droplets, biology and chemistry experimental procedures, microscopy imaging, and image analysis via software tools. The candidate will join an interdisciplinary and thriving environment comprised of international researchers and students, that work together and collaborate across departments and with other universities.

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The PhD candidate is expected to develop a machine learning method that combines in-situ, hydrodynamic models and remote sensing data to identify and forecast from coast to offshore patterns of the ocean dynamics (sea level, currents, waves etc.). That is essential for engineering and navigation purposes. This research is motivated by the Baltic Sea countries having access to an accurate high-resolution marine geoid model that synergizes different data sources to a common vertical reference datum, thus allowing continuous and accurate marine data from coast to offshore. Also, with the increased proficiency in computing technology and artificial intelligence (machine and deep learning methods) allows the exploration of various methods that synergizes different data sources, identification of patterns and forecasting of marine dynamics

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The PhD candidate is expected to develop dynamic vertical datum that is applicable in maritime and offshore engineering. The research takes a perspective for the entire Baltic Sea, with the objectives: i) develop a method for referring various marine data with respect to consistent and accurate geodetic vertical datum (marine geoid), ii) develop forecasting algorithms for determining spatio-temporally continuous dynamic topography variations. This is of vital importance for evaluating the energy potential, economic viability and engineering requirements of offshore renewable energy plants; also for optimizing shipping routes and improving the vessels clearance management. This research is motivated by the Baltic Sea countries having access to an accurate high-resolution marine geoid model that synergizes different data sources to a common vertical reference datum, thus allowing continuous and accurate marine data from coast to offshore. With the increased proficiency in computing technology and artificial intelligence (machine and deep learning methods) allows the exploration of various methods that in near real-time can determine the optimum route of vessels.

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Machine learning and Artificial Intelligence (AI) have found extensive applications across various sectors like energy and medicine. However, a major challenge lies in the lack of interpretability of these models, leading to distrust among users who prefer transparent decision-making processes. Explainable Artificial Intelligence (XAI) addresses this issue by making ML models understandable. The Department of Software Science seeks a PhD candidate to advance XAI techniques, focusing on developing novel frameworks that bridge mathematical complexity with human perception. This role aims to enhance trust in AI systems through improved interpretability, particularly in critical domains like energy, medicine, and cybersecurity.

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This research initiative focuses on digitizing and analyzing fine motor tests, crucial in diagnosing neurodegenerative disorders and assessing cognitive function. Despite recent efforts to employ AI, challenges persist due to the unique characteristics of these tests. The project aims to develop specialized deep learning architectures for analyzing drawing tests and human finger motions, with applications in diagnostics, cognitive assessment, and fatigue detection. The Ph.D. candidate will assess existing models, design tailored architectures, explore transferability between test types, and ensure compatibility with explainable AI methods. Additionally, responsibilities include teaching and supervision. Requirements include a Master's degree, proficiency in programming, and demonstrated interest in the research topic.

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This position focuses on leveraging AI-based methods to enhance the analysis of drawing tests for assessing fine motor skills and cognitive function, with applications in medical fields. The project aims to develop AI-powered digital twins capable of generating synthetic data mimicking human drawing behavior under varying levels of fatigue and cognitive development. Additionally, it seeks to develop twins for analyzing drawing tests to support differential diagnosis. Responsibilities include publishing results in top-tier journals, supporting teaching activities, and co-supervising students. Requirements include a Master's degree in relevant fields, proficiency in programming, strong analytical skills, and a demonstrated interest in the research topic.

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The main focus of this PhD project is to develop state-of-the-art data analysis and mapping methods that are efficient, reliable, and near real-time. The developed methods will play a key role in advancing methodologies for collecting, validating, and analyzing environmental data from challenging and hard-to-access environments (e.g., subglacial channels, underwater ecosystems, and abandoned mines). This project will concentrate on environmental data collected using various sensors and robot platforms designed and developed at the Center for Biorobotics at Tallinn University of Technology.

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The overall goal of the project is elaborating sustainable and up-scalable method for synthesis of thermoplastic cellulose derivatives using fully biobased reagents and mechanochemical action of reactive extrusion. The project addresses critical issue of providing bio-based alternatives for fossil-based plastics. Cellulose is the most relevant but strongly underutilized raw material for this. The project should provide solution for conducting synthesis of thermoplastic cellulose derivatives on sustainable and energy efficient way. The work is conducted in international team in collaboration with relevant academic and industrial research partners.

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