Open positions

The overall goal of the project is to develop an integrated management and assessment framework for municipalities and water utilities to reduce pluvial flood risks in urban areas. This includes analyzing the risks, mitigation and adaption measures and providing an integrated framework and decision support tools for increasing the resilience of urban areas. The project addresses the following research questions: What are the main hazards in urban areas triggering pluvial floods? What are the solutions (including technical) to cope with the challenges and increase resilience? How can the developed decision support tools be integrated to the current decision support process? How to pilot the developed solutions in real urban environment?

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Today, the setup of digital models is often a largely manual process, highly reliant on user interaction, being thus time-consuming and prone to human error and system/model misconfiguration. The data gathering automatization for digital twins will enable a reliable source of information to inform the multi-objective holistic design approach of the construction and renovation projects. Leveraging digital information and coupled workflows will help to improve on this aspect. This research aims to develop a conceptual workflow and test the prototype workflow on case structures for reality capture and reconstruction of information by analyzing data from existing databases and synthesizing it with new data. Data flow for delivering renovation and construction projects in the context of a highly fragmented industry is essential. For achieving the research aim, the project addresses the following research questions: • What approaches and methodology for reality capture of existing structures are available, and what are their benefits and limitations? • What kind of a workflow could be developed for reality capture and reconstruction of information for existing structures by utilizing data from measurements, existing databases and synthesizing it with new data? • What are the effects and how does the workflow benefit the different stakeholders?

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In order to feed the growing population on the Earth, the usage of pesticides in agriculture is inevitable. At the same time, the substances which are toxic for pests could similarly harm human health. Several previously used pesticides are found to cause serious health issues and now restricted in EU. The effects of long-term exposure of the pesticides we are using at the moment may have latent adverse effects and should be extensively studied. For example, increased incidences of the cardiovascular diseases are detected among the people who are working with the pesticides. The aim of the project is to investigate how the pesticides, found in our food, influence our cells and health. In our laboratory we have expertise in the energy metabolism regulation in the heart muscle and cancer studies. From this knowledge we want to move on, to clarify environmental aspects that could trigger pathological changes. For that we want to perform functional studies and map possible changes in cells energy metabolism in respond to pesticides.

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The overall goal of the project is the development of reliable spectroscopic and physicochemical methods in combination with multivariate analyses for the qualitative and quantitative assessment of lignocellulosic biomass and valorization products. The project addresses the following research questions: What are the main expected benefits and challenges of using spectroscopic methods together with chemometric analysis for qualitative and quantitative characterization of complex biopolymers? How to provide an effective extraction of the significant information from the multidimensional data and make reliable conclusions? How to improve the use of chemometric approaches in the field of biomass valorization?

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As modern societies are facing different transformative changes from coping with digital transformation to leading sustainability transitions, there is an acute need to think over also the role of public administration (PA) from its structures and processes to the capacities and values it provides. We look for prospective PhD students with relevant background and individual research proposals to study in detail (some of) these challenges.

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Emerging technologies in ICT allow real-time and near-real-time data collection in various aspects of urban scape, such as environment, traffic flow and asset management. This calls for structured approach on how to use this data in order to advance the quality of life for urban citizens, reduce the footprint of mobility and allow better planning for sustainable development. Advancements in 5G, IoT and edge technology allow to use drones for more than just transportation, but also for remote sensing. This needs classification of fields and defining KPIs to measure the performance.

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The goal of the research is building a high-quality hybrid system for explainable natural language understanding and finding areas of question answering where this approach outperforms both pure machine learning and classical knowledge representation/reasoning approaches. The bulk of the research consists of building and integrating machine learning, natural language semantics and symbolic reasoning components. The student will work as a part of a team already actively engaged in this work.

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This is a full time PhD position which will be supervised by both the Department of Business Administration and the School of Law. The principle supervisor will be Professor Istemi Demirag and co-supervisor will be Dr. Maria Claudia Solarte Vasquez from Law School.

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The main objective of the position is to research incorporating data analytic capabilities into the blockchain systems for digital forensics readiness. The candidate will also take a key role in the development of an innovative blockchain-based application in an interdisciplinary team.

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Platform work constitutes the fastest growing segment of alternative work. While many problems (e.g. low wages, unfair treatment of workers) have been reported, the more existential question of worth has received scant attention. Other than providing an (extra) income and a flexible schedule, we know little of what makes platform work worth doing, and why. This is especially true for low-skill and low-status platform work such as ride-hailing or delivery work (often labelled as gig work). The goal of this research project, thus, is to explore why gig work is considered work worth doing by those who have a choice. The question will be investigated through a single case study of Bolt drivers in Estonia. In Estonia, Bolt is not just another platform company, it is a huge start-up success story that is taught in business schools and regularly features in the national news. Thus, Estonia provides a well-bounded and interesting societal context to better understand the social construction of new forms of work as worth doing (or not) in the digital economy. The project is supported by the Estonian Research Council.

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The aim of this project is to investigate the properties of strain wave propagation through natural and synthetic felts. The proposed study is mostly related to mathematical modelling and numerical experiments.

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The project's overall goal is to examine, develop, and implement novel chemical methods and the best practices for soil health monitoring using portable analyzers and sensors supported by artificial intelligence. Implementing novel chemical methods for soil health monitoring will help farmers move toward the targets set by Green Deal. Specifically, Green Deal targets by 2030 include Farm to Fork and Biodiversity strategies, cutting nutrient losses by 50%, fertilizer reduction by 20%, chemical pesticides by 50%, and increasing the share of organic farming by at least 25%. As a result, this project will make agriculture more sustainable and precise by using smart technologies.

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Networks and their architectures are central to the mathematical and computer sciences as well as to systems biology and systems medicine. This proposal aims to transcend the concepts of network science onto synthetic chemistry and materials science. From the outset, we have identified that the concept of networks in chemistry should be divided into two separate, however, strongly intertwined research streams. We will design cell- permeable heteromultifunctional constructs to probe intracellular networks using small molecules that typically go beyond Lipinski’s rule. We will also build molecular networks (materials) by using single-precursor/fused hybrid linker-based approach and will apply them as electrocatalysts and in heterogenous catalysis for large-scale applications.

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Perovskites have revolutionized the emerging photovoltaic field achieving record-breaking efficiencies. The main objectives of PhD project are to apply the new method to synthesize emerging inorganic perovskites; to understand the potential of Pb-free inorganic perovskites for solar-to-hydrogen production technologies and design cost-effective high-performance fully inorganic perovskite PV device.

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Testing non-stationary systems, particularly autonomous systems, is inherently challenging due to the lack of test oracle. Metamorphic testing was introduced by Chen as a possible conceptual solution to test systems when a test oracle is not available. The behavioral properties of the system under test (SUT) are defined in the course of its state space exploration and specified using generic relations known as metamorphic relations (MRs) between different sets of inputs and their expected outputs. Objective is to develop methods for incremental construction of efficiently (run-time) decidable constraint sets that specifies SUT MR-based on its observable behaviors.

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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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The PhD position is opened for an Early State Researcher to be involved into materials science and tribological research of novel machining tool materials (inserts). These machining tools should be suitable for dry cutting of hard-to-cut materials. For their production the reduced amount of Critical Raw Materials should be used. Novel materials should reduce the waste of cutting fluids (usually used to cool down the tool, etc) and risk of health issues associated with use of such liquids. The main work will be associated with fulfillment of activities of European M-ERA.NET project.

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The overall goal of the project is to find new behavioral insights that explain financial and economic decisions of individuals. The focus of the research is to link psychological and/or health related factors of individuals to financial and economic outcomes and the heterogeneity in the outcomes. The new insights provide valuable policy suggestions to avoid or alleviate unsustainable financial and economic choices in the era of uncertainty individuals have been facing due to the COVID-19 pandemics and other changes in the environment.

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Rapid adoption of digital technologies accentuated by the COVID-19 lockdown regime has markedly changed the ways of working and the work-life balance of individuals. Extensive microdata research on individual level registry records and multiple cross-sectional survey data enables to investigate the effects of mental health and digitalization jointly and separately for better explanation of individuals’ labour market outcomes. The endogenous relationships arising in the interaction between labour market outcomes and mental health on the one hand and between digital skills and labour market outcomes on the other hand call for the application of modern econometric tools that allow to retrieve consistent estimates on the effects that either improve or impair individuals’ labour market outlook in the labour market conditional on their individual characteristics. This line of research offers valuable insights for the policy makers for improving the inclusiveness of the labour market and well-being of the individuals.

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The PhD project is dedicated to examining oceanographic and atmospheric 4-dimensional data (remote sensing, model re-analysis and climate projections) in the Baltic Sea region with the aim of uncovering hidden patterns and teleconnections of essential climate variables to advance the climate services for the decision support. The project has a scientific and a practical objective: (a) how to retrieve new scientifically sound regional marine climate information using big data analytics and; (b) how to present the relevant climate data to assure effective uptake of the value added information in the decision-making processes.

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Nucleus for European Modelling of the Ocean (NEMO) is a widely used model for the simulation of the ocean general circulation. Sub-mesoscale processes are not properly resolved with present model setups that are feasible for decadal time-scale simulations. A novel conceptual approach for the integration of a supervised machine learning method for the solution of horizontal diffusion term in the advection-diffusion equation of the scalar fields has been elaborated by our research team. The main aim of the research is to implement machine learning methods for the calculation of the horizontal diffusion terms in numerical solution of the advection-diffusion equations for water temperature and salinity within ocean general circulation model.

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The Baltic Sea is a complex marine system with several environmental problems. The role of circulation in shaping the state of the marine environment is not well understood. Uncertainties in linking the state of the marine environment with anthropogenic pressures and natural processes and predicting its future developments are largely connected to the shortage of understanding of the current regime in the Baltic Sea. This limited knowledge is at least partly related to the lack of joint experiments of in-situ current measurements and high-resolution numerical simulations. Particularly, understanding of temporal developments of currents in the submesoscale, synoptic (mesoscale) and seasonal timescales in the Baltic Sea needs improvement. The present PhD project addresses this shortage.

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The PhD position is opened for an Early State Researcher to be involved into additive manufacturing through selective laser sintering of metal-matrix composite materials, which are in-situ reinforced by ceramic phase. The principal of bio-mimicry will be applied to the designed structures to ensure their high wear resistance.

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In northern Europe, veneer and veneer-based products play an important role in hardwood species sustainable use. Birch is already widely used in veneer products, however lower value and underutlized wood species such as grey alder, black alder and aspen are not widely used in the veneer-based products. The reason for this is not the resource availability, but the lower quality of these wood species. For example, in the case of plywood, its mechanical properties (flexural strength and modulus of elasticity) depend on the type of wood, the quality of the wood, the moisture content of the wood, the density, the structure, the number of veneer layers, veneer thickness and glue. Density is generally considered to be one of the most important properties of a material because it is strongly correlated with the strength properties of wood. It is preferred to use in most construction structures high-density wood species due to their high mechanical strength. However, stocks of high-density wood species are limited and expensive. Therefore there is a need to start using the lower quality alternative hardwood species in the veneer-based products. Secondndly, the most wood species used for plywood production have poor resistance to swelling, biodegradation, fire resistance and UV weathering under outdoor and high humidity conditions. Therefore, when using plywood in conditions with high humidity, where regular wetting is possible, it is necessary not only to cover it from the outside with a hydrophobic laminate, but also to treat the single veneers constituting the entire plywood board. Prolonging the service life of wood and wood-based products results in a positive effect on greenhouse gas emissions by storing biomass carbon for longer periods.

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The overall goal of the project is to establish ground to investigate the electromagnetic (EM) emissions for the power electronic supplies and related electric installations. This will complement to the present regulations of electromagnetic compatibility, in particular, to oversee the bandwidth areas for monitoring and measurement to be used for diagnostics and overall reliability of the power supply. Practical EM near-field measurement methods can be expected to provide additional capabilities for enhanced diagnostic capabilities of systems and installations.

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The Ph.D. project is focused on the study of power electronic solutions for electric vehicle charger (EVC). The primary outcome lies in the development of EVC that would not only meet the today’s requirements, but will also be future-proof and applicable to mass installments. The study will cover analysis and improvements for well-known configurations, but will mostly focus on an emerging solutions.

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Overall goal of the project is to investigate the electric machine diagnostics using wideband transient traces of the PWM switching transients, in order to detect the imminent faults in the machine. Such condition monitoring would require more detailed analysis (especially in wide bandwidth) of the quantities of the electric machine, preferably at the output terminals of the frequency converter output. Within this research project, the capabilities of such system will be investigated, targeting for example, insulation failure and winding short-circuit failure.

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The Ph.D. project is focused on the study of energy management methods in the context of DC buildings and districts. The primary task is to research and develop control methods that would be able to manage DC grid supply and demand by integrating renewable generation, energy storage and flexible load management, with the aim of achieving energy neutrality or even positivity at the neighborhood level

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Flexibility is of prime importance for current and future Power Systems (PS) with increasing grid integration of renewable Energy sources (RES). In this regard, the main challenge is the management of the increased variability and uncertainty imposed by RES in the power balance. Contribution from the aggregated flexibility of nearly Zero Energy Buildings (nZEB) can significantly increase the flexibility of nearly Zero Energy Districts (nZED). This project aims to increase the ENERGY FLEXIBILITY of nZED as a key enabler of the transformation towards the high integration level of renewable energy resources in the grid. Most attention will be paid to developing a novel machine learning-based method, which accurately characterizes and exploits the aggregated flexibility from the demand-side to avoid excessive investments in conventional power plants and costs for balancing power.

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In light of fast technological changes and growing usage of LED and other outdoor digital screens (ODS) to spread information in form of commercials or announcements, the light pollution and comfort of people are more and more important to consumers of information and regulatory bodies. At the same time, in order to assure people about the safety and comfort of those technologies, the traceable and repeatable measurement method for ODS needs to be advised. Considering state of art studies and experimental approaches used for assessment of effects ODS to road safety, the novel lighting parameters assessment and measurement methodology should be developed.

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Additive manufacturing as a production technology opens new horizons in the optimization possibilities of electrical machines. Through various optimization algorithms improvement in machine performance characteristics as well as power density can be achieved. It is possible to optimize the magnetic circuits through which a maximal usage of magnetic material can be achieved, resulting in a low material waste technique to increase the power density of the electrical machines. Different lattice structures can be used in the magnetic circuit in order to assure constructional strength. In addition, lattice structures can be utilized in electrical machines teeth and poles, significantly improving the performance characteristics. However, there is a lack of knowledge on how these criteria can be fed to optimization algorithms. Also, there are no standard procedures, which would help to simplify and accelerate the optimization tasks and which would be described and matured enough to be implemented in industrial processes. In the project optimization methodology for electrical machines will be developed that would consider the advantages of additive manufacturing. The optimization will concentrate on optimizing the machine torque density and minimizing the losses in the machine. The optimization will be carried out together with prototyping and by validating the optimization model.

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A PhD position in the domain of electric microgrids and on the topic of local electricity markets jointly supervised by Tallinn University of Technology and Aalto University (cotutelle agreement). The aim of this research is to increase the economic feasibility of microgrids and individual prosumers by optimizing microgrid economic performance through participation in ancillary service markets and providing prosumers access to multiple electricity grid services.

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The PhD candidate will develop novel topologies of galvanically isolated dc-dc converters and corresponding hardware and digital control methods optimizing the converter operating mode.

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The project is aimed at solving vital environmental and economical issues – developing phosphate rock processing technologies. The most promising and environmentally least harmful methods for phosphate rock valorization are elaborated, focusing also on the extraction possibilities of valuable rare earth elements (REEs). The world's population is growing and needs more food. This means increased demand for phosphorus fertilizers. To do this, better technologies must be applied. The Green Turn means significant increase in the demand of different electric devices - from electric cars to wind turbines and batteries. Without rare earth elements this is not possible. So, finding possibilities for waste-free phosphate rock processing that enables to separate also REEs is a key to a better future.

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The overall goal of the project is to develop and optimize the non-toxic and earth-abundant Sb-chalcogenide absorber alloys for efficient thin film solar cells by using cost-effective and mass production close-spaced sublimation (CSS) technique. The project addresses the following research questions: What will be the most suitable processing approaches for the synthesis of these materials using low temperature, low cost and highly scalable techniques? How a semiconductor alloy system would allow tailoring the bandgap (from 1.1 eV to 1.8 eV) and current-voltage tunability required to design efficient thin-film solar cell configurations? How systematic material and device characterization can provide understanding of the physico-chemistry of the processes, properties of the materials and solar cells, and deliver insights for further optimization?

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The overall goal of the project is to develop and optimize the non-toxic and earth-abundant Sb-chalcogenide ((Sb2S,Se)3) absorber material and thin film solar cells, incorporating in the device structure new innovative hole transport layers. The project addresses the following research questions: What will be the most suitable deposition parameters for synthesis of hole transport layers and their incorporation in a thin film solar cell. How to identify the band alignment matching between antimony chalcogenide absorber material and hole transport layer? How to use complex characterization techniques to analyze and understand the transport mechanisms in such devices?

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This PhD position focuses at the development of thin films solar cells for semi-transparent applications containing earth abundant elements and exploring novel hole transport materials.

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This topic aims to contribute to the development of innovative hydrogen storage materials to contribute to the development of CO2 utilization and to help achieve climate neutrality in both the energy and transport sectors. The project studies the applicability of renewable natural resources (biomass) and waste streams (plastics) as raw materials for preparation of sorbent materials for hydrogen storage.

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This PhD position will be part of the Horizon Europe project ROBUST: Robust Crisis Governance in Turbulent Times – Mindset, Strategies, Evidence. The PhD project will address the following questions: 1) How does robust crisis governance differ from existing approaches to crisis management and resilience? 2) How can multi-level governance help or hinder robust responses to crisis and turbulence? 3) How have Estonian local governments experienced issues of multi-level governance, hybrid governance and societal learning in addressing the COVID-19 pandemic? 4) What are the lessons for future crisis management about adaptation and innovation of multi-level governance?

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This PhD position will be part of the Horizon Europe project ROBUST: Robust Crisis Governance in Turbulent Times – Mindset, Strategies, Evidence. The PhD project will address the following questions: 1) How does robust crisis governance differ from existing approaches to crisis management and resilience? 2) What has characterized the governance of recent financial, refugee and COVID-19 crises? 3) How does the hybridization of governance, democracy and law relate to robust crisis governance? 4) What are the potential trade-offs between legitimacy, efficiency and legality in hybrid crisis governance? 5) How have the interfaces between policymakers, experts and citizens impacted crisis governance?

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The goal of the project is to develop biophysical methods that can be used for testing lipase-drug interactions. The main disadvantage of the current methods is that the studies are performed in environments that are very different from those where lipases act. Therefore, many drug development projects fail. To achieve better results, it is necessary to develop methods that allow testing drug candidates in a close-to-native conditions. This PhD project involves testing different biophysical techniques and approaches to find most suitable ones for testing lipase-acting drugs under physiological conditions.

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The goal of this project is to better understand the interplay between geochemical and microbial processes occurring on the seafloor and in the shallow subsurface that control the growth rates and precipitation of FeMn-concretions. The study area is the Gulf of Finland and the aim is to identify key element sources and fluxes using detailed mineralogical, geochemical and isotopic characterization of the concretions via high-spatial resolution spectroscopic and mass spectrometric techniques. These data will be integrated with sediment and porewater profiling to determine redox changes and nature of microbial communities.

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The fourth industrial revolution requires humans to actively participate in the process of digital transformation of industry and manufacturing. As more services become digital, humans must remain at the center of the process. Meanwhile, the term “digital” can also refer to advancements in general artificial intelligence. As a result, the modes of interaction between the human and the “digital” become critical. This thesis topic is devoted to the concept of creating an intelligent immersive virtual environment in which the environment is built using digital twin and machine learning technologies and is perceived by the user in extended reality. The emphasis here is on the human-in-the-loop aspect: “how can coherent modes of interaction between the human and the digital environment be used?” which is particularly relevant in the context of Industry 4.0 transition towards Industry 5.0 human-centric concept and manufacturing applications.

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The ML models induced for these critical tasks can be bypassed or manipulated by the adversarial ML attacks. The attackers may create adversarial drifts, The defenders should detect these drifts in addition to the usual drifts occurring in the data distributions. This Ph.D. position requires the development of adversarial attacks and countermeasures for the cyber security solutions (e.g., malware detection, security monitoring) that are based on machine learning models. These countermeasures would include the detection of the adversarial drifts and inducing models that have the resistance to such attacks. It is possible that research may cover other adversarial attacks.

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Commercial buildings are among the main consumers of electrical and heating energy and traditional building automation solutions are nowadays not sufficient for efficient control. At the same time a lot of data can be obtained via BMS and IoT solutions. The research in the framework of this PhD project will be devoted towards data-driven methods and novel solutions for modelling and prediction of behavior buildings’ technical systems aiming development of energy efficient building automation solutions.

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The overall goal of this PhD project is to research methods and develop tools for real-time and offline processing of security events in SOC (security operation center) environments. The PhD project addresses the following research questions – in a production SOC environment, what are the most efficient methods for: (1) the reduction of false positive alerts, (2) the prioritization of alerts, (3) the detection of novel attacks? The purpose of this PhD project is to study the research questions on real-life data, validate research results in a production SOC, and release publicly available tools that implement the research results and advance the security posture of an organization.

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It is highly required to collect the scattered data from different sources and correlate and consolidate them to arrive at critical conclusions when cyber incidents are investigated in cyber-physical systems. In this PhD research, the main aim is to induce knowledge graphs from the digital forensic artifacts obtained from various system components, adapt knowledge-graph embedding approaches for the data and apply machine learning for downstream tasks. This research would consider using self-supervising approaches to obtain the optimal knowledge from the unlabeled data. Additionally, time and user embedding techniques would be incorporated into the knowledge-graph representations.

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Trace fossils are valuable tools for reconstructing past environments and benthic communities. This PhD project focuses on Paleozoic trace fossils in the Baltoscandian basin, which are yet poorly documented and understood. The project will advance ichnotaxonomic research, establish a regional ichnofossils database, and analyse the spatial and temporal trends in ichnofossil diversity and disparity. It is expected that comparison with climate reconstructions, environmental proxy indicators and shelly fossil groups allows identifying links between the evolving trace fossil communities and environmental conditions. The successful candidate will be employed as an early-stage researcher at TalTech with a net income starting from €1200.

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Sustainable thermoplastic polymers are developed and investigated to partly or fully replace non- renewable-resources-based materials for melt processing technologies. Cellulose appears to be a nearly unlimited renewable resource for polymeric materials. Discovery of dissolution processes of cellulose in ionic liquids opens several new routes for functionalization. Thermoplastic cellulose derivatives can be prepared without use of plasticizers, by attaching long-chain ester branches to the macromolecule of cellulose. Based on circular economy and sustainable environment principles the functionalized cellulose based thermoplastic composite materials are considered as viable solution for commodity products. The research challenge is to design the cellulose thermoplastic composites to enhance the mechanical properties of the novel thermoplastic materials. A laboratory line for pilot production of the novel thermoplastic materials will be started. Producing and processing parameters of the novel materials will be clarified for further commodity applications.

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Current project deals with CO2 reactions with small organic molecules via CH-activation enabling access to a novel platform for future chemical industry. Thus, metal-organic framework (MOF) based catalysts will be developed for CO2 addition to alkyl groups of various organic compounds leading to novel feedstock materials.

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Lytic polysaccharide monooxygenases (LPMO-s) are an important class of biotechnological enzymes that play a crucial role in the second generation biorefineries. It's been estimated that the cellulose-degrading enzyme cocktails used by today's biotech industry are made up of close to 20% of LPMO-s by weight. Since many technological processes occur at elevated temperatures and extreme pH environments, there has been a rise in interest to sourcing those enzymes from organisms that can withstand extreme conditions (high temperature, alkalinity, etc.). The primary goal of current doctoral thesis is to isolate and characterization of LPMO-s from thermophilic Actinobacteria utilizing structural biology and enzymology methods. The overall goal of the project is to provide a thorough insight into the structural and functional elements that are involved in ensuring both temperature stability as well as substrate specificity of the enzymes under study. Although in silico methods have come a long way (e.g. AlphaFold) accurate validation of structural nuances within a proposed model can only be achieved by the utilization of physical structural biology methods (X-ray crystallography or CryoEM). The kinetic models for these enzymes can be rather complex due to the requirement of hydrogen peroxide and an external source of electrons (e.g. through the coupling to oxidation of lignin derived phenolics) to carry out effective cleavage of glycosidic bonds within the crystalline cellulose polymer matrix. Therefore, tweezing out the details with respect to the kinetic models of these enzymes for their utilization in complex enzyme mixtures that are commonly used by industry, will be paramount.

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The overall goal of the PhD project is to investigate if – and respectively how – advanced digital business applications, product and process innovations in this field, and related change management activities can be developed to create a two-way pipeline of data and information to deal with future sustainability topics that present challenges for businesses and their decision-makers. The project examines the following research questions: What are the main perceived synergies and/or conflicts between digital and sustainability business transformation? How relevant are formal business characteristics like industry, firm size or firm age regarding such synergies and/or conflicts? What are the roles of internal and external stakeholders concerning such synergies and/or conflicts?

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New sustainable methods are needed to decrease hazardous waste generation during synthesis of active pharmaceutical ingredients (API). Macrocycles, efficiently synthesized by benign methods, and which are capable to act as selective receptors for removal of hazardous organic compounds are needed. We are looking for a motivated PhD student willing to contribute to the development of new mechanochemical synthetic methods for preparation of such receptor molecules, which are suitable for capture of organic pollutants based on supramolecular (host-guest) interactions. The PhD project will also include development of remediation pathway for side products in API production, that employs a mechanochemical conversion of the captured pollutants as an energy-efficient and low- waste technology, which comprises a facile product isolation protocol, fast reaction rates, easy recovery and recycling.

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The focus of the PhD project is to examine the possibilities for reconstructing the local past climate at fine resolution and reveal the previously unknown aspects of past seasonality and dynamics of the length of the vegetation period. The main research topics are the postglacial climate change, local vegetation and humidity changes and the comparison of postglacial local and regional climate dynamics in Central and Northern Europe. The research questions will be addressed using the plant macrofossil analysis, peat analysis and microphenological analysis of the subfossil leaves. The PhD project will be carried out in international cooperation and under the supervision of the researchers of TalTech, Utrecht University, Poznan University and Polish Academy of Sciences.

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The aim of this research project is to develop model for large-scale heat pump integration into district heating and cooling system. This model should handle variable parameters of heat sources (waste heat, seawater, solar heat, etc.), heat pump configurations and operating conditions for district heating and cooling. The project will address following scientific questions: What will be the effect of heat pumps internal heat loss reduction on the efficiency of district heating/cooling systems? What is the realistic HP capacity potential considering the proximity to existing DH areas? Which investments are required, when large heat sources should be used to feed into existing DH? What is the potential of large heat pumps in district heating and its role in future energy sector in Estonia?

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Increasing use of building automation and sub-metering of energy use provides the opportunity for advanced energy use analysis and application of new business models. The PhD project focuses on developing data-driven methods for modelling the energy use in existing non-residential buildings that will lead to more efficient technical systems. The developed methods will be applied to (1) identify abnormal energy use patterns in buildings for quality control during operation, (2) optimize the operation of heating, ventilation and air-conditioning (HVAC) systems, (3) quantify the impact of energy saving measures after implementation.

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The topic of this PhD project is homotopy type theory, a logical framework for mathematics, particularly suited for homotopy theory and higher category theory. This PhD project investigates the expressivity of homotopy type theory as a foundational system for the development of constructive mathematics, especially the interaction between higher inductive and coinductive types with semiclassical principles.

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Symbolic finite automata extend classical automata by allowing infinite alphabets. In these automata predicates over infinite alphabets, which form a Boolean algebra, serve as labels of transitions. Symbolic automata have been studied intensively in recent years, because they solve some practical problems for which classical finite automata are unsuitable. However, the study of symbolic automata is a young field and much work is still to be done.

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Alzheimer’s disease (AD) is an age-dependent neurodegenerative disease with enormous personal tragedy and socio-economic concerns. AD is multifactorial disease and despite intensive research, the reasons of AD are still unknown. There is an increasing number of facts pointing to the relation of AD with metabolic disorders of copper in the brain. It is generally known that copper levels in AD brain are decreased especially in amygdala and hippocampus, however, extracellular copper levels are increased. Elevated copper content may be associated with the formation of amyloid plaques as in vitro experiments demonstrate that copper can bind to Aβ peptides, the major constituents of amyloid plaques, and initiate their aggregation. Similar processes may take place also in brain as amyloid plaques contain up to millimolar concentrations of copper ions and it is hypothesized that copper ions, bound to amyloid plaques are toxic to the neurons. Drosophila melanogaster is a convenient model for studies of neurodegenerative diseases where different disease causing genes could be expressed in nervous system or in fly eye cells. Major aim of the current project is finding relationships between copper metabolism and AD phenotype in different Drosophila melanogaster strains and finding molecular tools for its regulation.

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The world has been recently shaken by major crises including Covid-19 pandemics and the Russia-Ukraine war. The policy making, however, is also challenged by the path-breaking global regime changes such as climate change, mass migrations, digital and technology transformation or soaring public debt. Whether anticipated or not, these challenges require reliable estimates on the prospective effects of these events/processes or on the consequences of planned policy interventions. The research in causal modelling has made rapid progress. The context dependence of causal methods, however, calls for more applied research that employs, integrates and possibly augments the existing tools, while comparatively mapping the strengths and weaknesses and suggesting new insights on the applications of these methods in particular settings.

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A research team working on industrial strategy and competitiveness using innovative data analytical methods and tools is looking for a PhD candidate interested in economic complexity and global value chains. The PhD candidate would join the two on-going international projects, carried out in co-operation with outstanding research partners.

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The goal of the project is to examine, assess, and develop further the methods to analyse tumor genomes at the level of exomes, full genomes, and transcriptomes and quantify the extent of useful information that can be gained from samples where paired normal tissue is not available. As there exists a large body of tumor samples, particularly FFPE samples, where detailed clinical information is available, including long term patient outcomes, but often normal tissue control is missing, this project will address the need to bring the genomic information contained in these samples into use for clinical and translational research. The project is implemented in collaboration with the North-Estonian Medical Center and initial focus is on lung cancer.

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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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Insulation degradation is an unavoidable issue in power components which get accelerated by different operational and environmental stresses. This PhD project aims at numerical modelling of insulation degradation process and statistical analysis of the distinctive features of partial discharge signals based on efficient data processing technique.

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The aim of the PhD project is to develop novel control methods for nonlinear dynamical systems. The developed methods will be based on combining the flatness-based feedforward control with the ideas from event-based control approach. There are different theoretical and practical aspects that the PhD candidate can study within the project.

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A PhD position in an organic chemistry group that focuses on the development of sustainable synthetic methods in continuous-flow. In the course of this project, a PhD student will be involved in merging asymmetric organocatalysis with electrochemistry in flow. Asymmetric catalysis plays one of the most important roles in the modern organic chemistry providing methods for the synthesis of biologically active compounds and pharmaceuticals.

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The PhD project will focus on the design, synthesis and application of multifunctional enantiomerically pure organocatalysts. New catalysts possess several halogen bonding, hydrogen bonding and Lewis basic sites mimicking active sites of enzymes.

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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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DNA replication is one of the major targets of cancer therapies, as cancer cells tend to proliferate faster and are generally more prone to replication stress. In addition, mutations in or misregulation of certain replication proteins have been implicated in cancer development. This project aims to study cancer-associated mutations in replication proteins and their effect on DNA replication initiation, DNA synthesis and cell cycle in cultured human cells. Methods: CRISPR-Cas9 genome editing, cloning, mammalian cell culture, flow cytometry, transfections, siRNA, survival/proliferation assays; co-immunoprecipitations, pulldowns, western blots, proteomic screens.

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In heart muscle cells, energy used by most of the processes in the muscle cells is supplied in the form of ATP by mitochondria. Within this project, you would be designing state-of-the-art mathematical models to unravel mechanisms regulating ATP production in mitochondria, selection of energy transfer pathways between mitochondria and ATP consumers, as well as working on mechanistic models of chemical energy conversion into mechanical work by actomyosin. As you will be working in an interdisciplinary laboratory you will have access to the experimental data measured on-site and will be given the opportunity to design and participate in the experimental work to support your research.

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In heart muscle cells, calcium regulates cells' contraction and mitochondria energy production needed to perform mechanical work and maintain ion balance. The primary calcium source in adult mammalian cells is the sarcoplasmic reticulum (SR). Recently, it has been shown that SR and mitochondria are physically linked and regulate mitochondrial respiration. The precise interaction between them is essential for maintaining energy balance in the heart, yet many aspects of this regulatory pathway are still poorly understood. This project aims to unravel mechanistic aspects of SR-mitochondria interaction in heart muscle cells.

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Ragnar Nurkse Department of Innovation and Governance is opening a doctoral position in the research area of 'Critical understanding in predictive policing.' The potential doctoral researcher is invited to submit his / her more specific research project, which is in line with the main research axes of the NordForsk project, from which the position is funded. Notably, the project should focus on theoretical and empirical research that contributes to establishing transparency and set an epistemological standard for the critical investigation of innovative data-based policing.

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