Open positions

The overall goal of the project is to examine how urban food systems and sustainable transport systems interact spatially and temporally in the context of 15-minute cities and transit-oriented development. This early stage researcher (PhD) position is part of the TalTech-led research project Hyping Agriculture and Transit (HAT) in 15-minute Cities (15mC), which explores how food-growing, public transport-oriented communities can support urban transitions as green Proximity Oriented Developments (PODs).

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Georesource production is part of the foundation of our modern society, yet obtaining the social licence to operate (SLO) is particularly complex. Such projects often attract strong public opposition (also known as NIMBY or BANANA attitudes). At the same time, the reasoning and, moreover, solution ideas of opposing individuals are little researched. This is partially due to the high complexity of quantifying reasoning during classic surveys. The rise of Generative AI (GenAI) provides the technological leap that allows us to survey the reasoning of individuals and quantify it in an economic way. The aim of this project is to explore the possibilities of GenAI-supported surveys to understand the reasoning and solution ideas of stakeholders and to obtain an outlook for improved stakeholder inclusion processes.

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The main objective of the research is to study and develop the AI based Production Processes Optimization and Management System. The main activities of the research are: modification of production monitoring system Dimusa; process optimization and management system prototyping; analysis, forecast and visualization modules. The production process optimization and management system have data collection, analysis, visualisation and data storage modules. As each production system has its own specifics with a huge variety of possible modifications, the system should be flexible for modifications. The trends are: web-based architecture, re-configurability, near real time performance, open-hardware and software, wireless connectivity, self-learning with predictive functionality, supported by cloud computing. The tasks of the work is also to create a digital twin of the production processes, which includes the movement of the materials and labour along the production path and processes. During the work a 3D virtual simulation model and digital-twin will be developed to describe production processes use cases and key indicators and to define a solution method for an artificial intelligence-based management system. During the work, the solution method will be validated and tested using real data from the production system based on the input from food-, wood- or machinery industry. Based on the digital twin, the user interface of the Dimusa operative monitoring system will be created, which uses the functionality of artificial intelligence to control and optimize the tasks and activities of production processes at different stages.

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The Department of Electrical Power Engineering and Mechatronics at TalTech invites applications for a fully- funded PhD position in the field of AI applications in electric power systems, with a special emphasis on multimodal AI models and their applications in electric power systems. This interdisciplinary PhD position combines artificial intelligence (AI) and modern electric power systems. The primary objective of the research is to investigate and develop multimodal machine learning models capable of processing heterogeneous data sources—specifically time-series sensor readings, visual data (e.g., images and video streams), and unstructured textual documents. As electric power systems undergo digital transformation, the integration and intelligent analysis of such diverse data modalities are increasingly critical for operational reliability, predictive maintenance, system diagnostics, and grid optimisation. The PhD candidate will contribute to developing robust, interpretable, and scalable AI methodologies tailored to the requirements and constraints of power system applications.

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This PhD project focuses on the metabolism, pharmacokinetics, and toxicology of novel and emerging psychoactive substances (NPS), combining in vivo and in vitro models with cutting-edge analytical technologies. In cooperation with Jena University Hospital, Institute for Forensic Medicine, Friedrich Schiller University Jenaand Tallinn University of Technology spin-off venture SafePAS, the project will develop advanced methodologies to detect and understand the biological behavior of new drugs, supporting the creation of fast and accurate field-testing devices for law enforcement and healthcare. The candidate will be part of an interdisciplinary and international team addressing the societal and scientific challenges posed by the rapid emergence of synthetic drugs.

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This PhD project explores the intersection of responsible artificial intelligence (AI) in high-contact, human-centered services such as education, healthcare, banking, etc. As AI becomes integral across service sectors, ethical concerns have emerged as the most significant theme for practitioners and researchers. This project aims to understand how AI technologies are being ethically integrated into service design and delivery. Some examples include the use of AI in personalized learning, admissions, and student evaluations, AI-driven diagnosis and treatment recommendations, and AI in sensitive personal financial data handling, highlighting AI's criticality in consumers’ trust, safety, and privacy. This research emphasizes AI-enabled services' ethical, social, and operational impacts on customers. The research will advance knowledge of AI-enabled, responsible service design and delivery that enhances customer experiences while ensuring fairness, transparency, and accountability. The societal and managerial implications of the research will provide frameworks to service providers to build trust through fairness, transparency, and responsibility while delivering AI-enabled services.

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The project focuses on the interrelation between transnational migration flows, entrepreneurial agency, and urban placemaking in Tallinn by studying the (in)formal entrepreneurial practices of underrepresented migrant groups, whose presence in urban space is simultaneously marked by visibility and invisibility. By integrating theoretical frameworks from entrepreneurship and urban studies, the project explores how the spatial and temporal dynamics of urban transformation generate both opportunities and constraints for these practices, and, respectively, how urban (in)formal economies are shaped by the marginalised agencies. This is expected to advance the conceptualisation of contemporary entrepreneurial identities as they emerge from peripheral migrant experiences shaped by neoliberal urban dynamics. While primarily intended to employ a qualitative methodology, the project can be enhanced through a mixed-methods approach (quantitative and/or spatial data). In Tallinn, a second-tier capital city geographically situated on the margins of the European Union, a small but growing community of “new” migrants from geographically and/or culturally more distant areas is emerging. Given the uncertainties of the time, the increase of these groups and their implications for society and the economy are to be expected. While the theorisation of related processes is largely developed on such migrant entrepreneurs in cities and metropolises of the “old” Europe, the academic research often overlooks the perspective of Eastern European cities, which have only relatively recently begun receiving more of those migrant groups. This project, with a specific focus on (in)formal entrepreneurial activities, aims to generate deeper insights into the socio-cultural and economic implications of these new and underrepresented migrant groups on urban life and economies in cities like Tallinn. The research is expected to offer relevant input for urban governance to better respond to the needs of an increasingly diverse urban population and to support their integration by enhancing both their socio- cultural wellbeing and economic contribution to the Estonian society. Drawing on a relational concept of space (e.g. Löw 2018; Amin and Thrift 2002; building on Lefebvre 1974) and migrant entrepreneurship studies (Kloosterman et al 1999; Kloosterman and Rath 2001; Ram and Jones 2008), the project would also support a more complex consideration of the unique and dynamic ways in which these groups contribute to the social creation of urban space.

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In the context of accelerating digital transformation and growing sustainability imperatives, technology-driven business models are undergoing profound innovation. The project aims to explore how digital services can drive sustainability while scaling globally and how sustainable innovation in technology-driven models in general interacts with international business expansion. The applications for this study can be mobile banking, decentralized finance, blockchain, decentralized autonomous organization, AI, technology ventures and others. Examining the interplay between sustainability-oriented service innovation and institutional, regulatory, and cultural dynamics across borders, with a particular focus on risk management strategies and addressing uncertainties in international markets will be conducted by comprehesive qualitative and/or quantitative methodology and international comparative analysis. The project outcomes will contribute to business models studies, innovation capabilities, risk mitigation approaches and ecosystem partnerships that facilitate sustainable international growth in fintech and other technology driven businesses. Research will contribute to theory at the intersection of international business, service innovation, risk management, and sustainability transitions.

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The overall goal of the project is to experimentally examine the opportunities involved in using large language models and other AI applications in domains of business. The focus is on building virtual assistants to automate various business processes. The effectiveness of such automations is measured by computational methods and by pilot testing in an actual business context. The project resides in the field of business information systems, which is an overlap of software engineering and business administration. The project addresses the following central research question: How can large language models be effectively developed and integrated as virtual assistants to automate and enhance business processes in real-world organizational settings?

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This project explores the emerging DefenseTech entrepreneurial ecosystem in Europe, with a specific focus on how startups, corporates, intermediaries, and governments collaborate to foster security innovation and societal resilience. The research examines the dynamics and institutional arrangements that enable or constrain entrepreneurial initiatives in the defense and dual-use technology sectors. By analyzing case studies across key European hubs and applying theories from entrepreneurship, innovation systems, and institutional theory, the project aims to deepen our understanding of how new ventures contribute to strategic autonomy and technological sovereignty in a rapidly evolving geopolitical landscape.

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This PhD position at TalTech School of Business and Governance focuses on investigating the rapidly evolving landscape of ESG considerations within the global business context. The research, conducted within the Department of Business Administration and potentially collaborating with the Center for Responsible Economy and ESG, can explore diverse areas such as ESG reporting harmonization across different regions and company sizes, the economic impact of mandatory ESG disclosures, the quality and assurance of ESG information, the connection between ESG factors and financial performance, or how investors utilize ESG data in their decision-making in all over a supply chain. The primary goal is to produce a high-quality doctoral dissertation based on independent research within these dynamic fields of ESG, ESG reporting, and/or international accounting.

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The Department of Electrical Power Engineering and Mechatronics at TalTech invites applications for a fully- funded PhD position in the field of AI applications in electric power systems. This project aims to develop privacy-preserving, transformer-based federated learning models tailored for power systems. As smart grids increasingly depend on data-driven intelligence, preserving data privacy across distributed sources like smart meters and substations is a growing challenge. Federated learning allows collaborative model training without centralising sensitive data, but integrating complex architectures such as Transformers while maintaining privacy and efficiency requires further research. This project will explore secure and scalable AI techniques that enhance grid analytics without compromising user confidentiality.

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The PhD project aims to design and synthesize innovative nanocomposite materials tailored for antimicrobial applications in biomedical fields, including wound dressings, implants, and surface coatings. This research will focus specifically on creating novel nanomaterials by combining metal-based antimicrobial nanoparticles (e.g. Ag, CuO, ZnO) with biologically active organic compounds (e.g., chitosan) to achieve synergistic antimicrobial effects. The most promising combinations will undergo comprehensive assessment for human and environmental safety. The central research question guiding this doctoral thesis is: how do specific combinations of metal-based nanoparticles and organic compounds influence their antimicrobial effectiveness and safety?

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Indoor photovoltaics (IPV) have significant 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 objective is to customize the optical and electronic properties of the absorber material, as well as the device architecture, to better align with indoor light sources such as cool and warm LEDs.

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Wind induced surface‐wave breaking injects bubble plumes that mediate key air–sea exchanges, yet their subsurface structure is presently sampled only at limited locations by acoustic instruments. Measuring on the large scale is often impractical due to high maintenance costs. Alternatively, satellite Synthetic Aperture Radar (SAR) offers high-resolution spatial overview independently from daylight or cloud coverage. This PhD will develop and benchmark machine-learning models—from classical methods to deep networks—to translate SAR imagery into quantitative bubble-plume properties and establish the spatial and temporal limits of these retrievals. Outcomes will support a broader effort to derive air–sea fluxes from remote sensing observations.

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This objective of this position is to advance the theory and applications of skew monoidal categories, variations and specializations thereof, related structures such as skew multicategories and higher-dimensional generalizations.

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A fully funded four-year PhD position is available for an Early-Stage Researcher in the field of additive manufacturing, with a specific focus on the design, development, and laser powder bed fusion (LPBF) of Ni₃Al intermetallic-based superalloys. Ni₃Al-based superalloys offer superior mechanical performance compared to conventional Ni-based superalloys due to their extra high content of strengthening intermetallic phase. These materials are particularly well-suited for high-temperature applications, such as components in the hot sections of aero engines, where mechanical strength, thermal stability, and low density are essential. However, the additive manufacturing of these alloys presents processing challenges -such as cracking and defects - that require a careful and comprehensive approach to overcome.

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Renewables have turned the tables in the electric distribution networks (DNs), where already today strict limitations are in effect to limit the actual renewable-produced energy infeed. Significant proportion of these limits are imposed due to classical approach to low-voltage distribution systems, where the network operates in a static, unobserved realm. This project aims to widen the DN actual performance characteristics through a more massive operating state observation leading through to impressive range of smarter-grid applications, such as enchanced true renewable hosting capacity, intra-community energy delivery provisions, greater and more effective flexibility of the grid etc. In this stage, emphasis will be put on aspects of general power delivery reliability and network topology identification thorugh operation characteristics modeling. The range of questions leading the path could be: how to implement measured data for network physical characteristics determination? where to set up the measurement systems and how many of units there would need to be? how to determine the actual loading and load carrying capability based on measured data?, and many others.

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Aim of the PhD project is to develop a setup of ocean models for modelling the biochemical processes in the Baltic Sea, including the coastal waters of Estonia. Successful PhD applicant will be working with numerical ocean models at HPC centers in TalTech and EuroHPC machine LUMI and collaborate with other ocean modellers around Baltic Sea.

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This research project aims to explore how individual psychological traits interact with external conditions to shape financial and consumption behaviors. Using validated survey tools linked to extensive Estonian registry and transaction data, the study will provide empirical insights into real-world decisions related to investment, risk-taking, saving, and spending. Key themes include the role of cognitive and attitudinal factors, behavioral biases, social influences, and responses to income shocks, with the goal of bridging behavioral finance theory and household finance models.

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Recent developments in enzyme engineering have leveraged advances in machine learning, directed evolution, and synthetic biology to design more efficient and specific biocatalysts. These innovations have significantly enhanced the ability to tailor enzymes for industrial processes, from pharmaceutical synthesis to sustainable biofuel production. The importance of enzyme engineering lies in its potential to replace harsh chemical processes with greener, more sustainable alternatives, contributing to cleaner manufacturing and environmental preservation. Despite progress in enzyme engineering, a systematic approach to analyzing enzyme production—spanning construction, expression, and activity—remains underdeveloped. To address this gap, the PhD project plans to implement a high-throughput platform that enables rapid construction, expression, and screening of enzyme variants for comprehensive performance analysis. The project is carried out in close collaboration with AS TFTAK - an independent Research Institute in Tallinn, Estonia.

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This PhD project examines real estate tokenization—using blockchain‐based digital tokens to enable fractional property ownership, reduce intermediaries, and enhance market access and transaction efficiency—by filling the gap in empirical research on its practical performance. Leveraging on-chain transaction data, it will analyze investor behavior (trading frequency, holding periods, diversification), assess secondary-market liquidity through trading volumes and bid-ask spreads across platforms, and investigate price discovery and volatility in response to economic events to determine whether token prices track underlying real estate values or resemble more volatile crypto assets. The goal is to evaluate tokenization’s impact on market efficiency and inclusiveness while identifying persistent challenges in liquidity and price stability.

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Electrical machines are the workhorses of modern industry. Thus, electrical machines are facing challenges in meeting very demanding performance metrics, for example, high specific power, customization, etc. This provides clear motivation to explore the impact of new manufacturing methodologies and its possibilities to enhance electrical machine performance. The use of additive manufacturing to produce electrical machines gives several new possibilities how to design more efficient and higher power density machines. However, to achieve the maximum effect in the design of the machine, it is necessary to consider also the control methods through which it is possible to increase both machine efficiency and controller efficiency. The overall goal of the project is to develop the control methodology for additively manufactured switched reluctance machines. The project focuses on optimizing the selection control method according to the machine design. Also, the control method will be used as an input on the electrical machine optimization to achieve high energy-efficient drive system. The practical part of the work will be testing the control model with a prototype machine in the research laboratory.

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Additive manufacturing (AM) is evolving rapidly, and it is seen as an important step towards the next industrial revolution, being one of the key requirements for the decentralized production of highly complex structures. The flexibility of AM technology also allows the production of electromechanical components and electrical machines, which can have significantly better properties compared to conventionally manufactured devices. AM opens the possibility to utilize unconventional three-dimensional topology optimized structures, which allow the production of novel heat exchangers (HE) for electrical machines. However, currently there is no existing well-developed design methodology to realize these advantages. Therefore, this project focuses on the development of a design methodology for topology optimized HEs considering the advantages of AM. The objective of the project is the selection of an innovative electrical machine HE design, and the development of the optimization methodology. The practical part of the work involves validating the methodology in the lab by additively manufacturing (3D printing) the developed cooling solution and performing real-world measurements.

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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 innovative 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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Financial vulnerability can refer to the difficulties people face in everyday life to pay bills (Loke, 2017) , cover unexpected expenses (Lusardi et al., 2011) or to maintain their lifestyle (O'Connor et al., 2019) . Irrespective of the precise definition, financial vulnerability can have adverse impact on the financial well-being of individuals.

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This PhD position is part of the UrbanSplash project under the Smart City initiative, aims to revolutionize the monitoring of bathing water quality (BWQ) by addressing critical limitations in current systems. Existing methods for detecting faecal indicator bacteria (FIB), such as E. coli and enterococci, are slow, often taking 18–72 hours or more, which delays risk mitigation and fails to protect public health effectively. The research focuses on developing innovative solutions combining low-cost sensors with advanced machine learning (ML) and physics-informed neural network (PINN) models to provide real-time, high-resolution BWQ data and spatial predictions. By integrating multiple data sources, including real-time sensor readings, meteorological forecasts, and hydrometric data, this work will enable rapid detection and forecasting of pollution events, improve public health safety, and promote sustainable use of urban water bodies. RQ1) What is the most reliable and accurate calibration relationship between the laboratory analysis and the hourly sensor measurements? RQ2) Which machine learning models are suitable for hourly and daily forecasting of microbiological water quality including sensor measurements and site specific physical environmental and weather data? RQ3) How does the new modelling platform improve and inform public health protection measures as they relate to European Bathing Water Directive?

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Retrofitting existing fossil-fired fluidized bed power plants with high-temperature thermal energy storage systems is a promising approach for the flexible CO2-free provision of electricity and heat. This however, requires the modification of the existing fossil-fired fluidized bed power plants to be identified. The best approach for identification of the necessary modifications is to use the numerical methods, including computational fluid dynamics (CFD) and semi-empirical dynamic models based on the 1.5-D approach. The main aim of the PhD project is to develop different numerical models and validate these models using experimental data. The PhD project is supervised by Senior Researcher Dmitri Nešumajev and co-supervised by Professor Oliver Järvik (Department of Energy Technology, Tallinn University of Technology, Estonia).

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The R&D portfolio of the research group focuses on the development of emerging inorganic chalcogenide thin-film photovoltaic (PV) technologies for specialty PV markets, including building-integrated photovoltaics (BIPV), product-integrated photovoltaics (PIPV), PV-powered IoT applications, and future green responsible optoelectronics. This PhD research topic focuses on indoor PV applications, as one of the fastest growing PV market among non-conventional PV markets. 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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The overall goal of the project is to improve the mechanical and physical characteristics of low-quality hardwood species by the densification process, thereby increasing their value. Sustainable chemical modifications and energy efficient low temperature methods will be investigated for densification process and properties will be evaluated. As a result, more low-quality underutilized wood species will be used to create innovative engineered wood products. The project addresses the following research questions: What are the suitable green chemistry approaches for wood densification? How low temperatures can be used for wood densification? Does all the wood species behave the same way in densification process? What are the effects of dedication to wood structure and properties? How to enhance the wood densification process for more efficiency?

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This PhD project aims to develop a sustainable, energy-efficient treatment process for removing contaminants of emerging concern (CECs) from municipal wastewater and urine for safe agricultural reuse. The research focuses on gas-phase pulsed corona discharge (PCD), a non-thermal plasma technology with superior energy efficiency compared to conventional advanced oxidation processes. Key objectives include optimizing selective CECs degradation, achieving effective bacterial control, and assessing the environmental and economic feasibility of the process. The position offers the opportunity to work on innovative plasma-based water treatment technologies within a wider initiative promoting sustainable water reuse and circular nutrient management in agriculture.

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The goal of this doctoral project is to develop and evaluate integrated planning and forecasting systems for distribution networks, with a particular focus on collaborative models that improve both sustainability and operational efficiency. The research will explore key questions such as: 1. What are the main limitations in existing logistics planning and forecasting systems? 2. How can combining demand forecasting with tactical planning improve logistics performance and reduce environmental impact? 3. What helps or hinders the use of collaborative distribution models in both urban and rural settings? 4. Which models are most relevant to the Estonian and Baltic logistics markets?

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The PhD candidate will participate (together with the supervisors and project team) in the research and design of a prototype of a next-generation mobile sensing platform tailored for environmental monitoring in urban areas. The platform will integrate multi-modal sensors, on-device signal processing, and lightweight anomaly detection algorithms for real-time operation on mobile assets (e.g., public transport vehicles or garbage trucks). The final system will seamlessly integrate into the digital infrastructure supporting smart city data governance.

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Defects have a strong impact on the performance of thin film absorber materials and solar cells. We are looking for a candidate to work on the theoretical modeling of materials (Sb2Se3, Sb2S3, Bi2S3, and their related alloys) and solar cell devices, with the goal of supporting, guiding, and accelerating experimental development. This work will require the use of ab initio density functional theory (DFT) techniques, as well as drift-diffusion models for simulating charge transport in thin-film solar cell structures, along with other relevant modeling approaches. As the research group currently lacks dedicated expertise in theoretical modeling, the successful candidate will become the in-house expert in this area, offering a unique opportunity to establish an independent research platform while working closely with the experimental team and gaining hands-on experience with a range of physical and chemical thin-film deposition techniques. The candidate will join a diverse and collaborative research environment, with the opportunity to be actively involved in the COST Action CA21148 – ReNewPV. Through this network—and specifically via the dedicated Materials and Device Modeling Working Group in ReNewPV—we will ensure strong collaborative ties (including potential joint supervision) between the Laboratory for Thin Film Energy Materials at TalTech (Tallinn, Estonia), the Photovoltaic Group (Prof. Dr. Zacharie Jehl Li-Kao) at the Department of Electronic Engineering, Polytechnic University of Catalonia (UPC – BarcelonaTech), and the group of Prof. Keith McKenna at the University of York, UK, who also leads Working Group 2 in ReNewPV. The successful applicant will have a unique opportunity to conduct research visits (Short-Term Scientific Missions) at UPC, the University of York, and other collaborating institutions.

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The Baltic Sea is a semi-enclosed sea whose water levels are influenced by a mix of global and regional factors, making it an ideal natural laboratory for sea level studies. This PhD project aims to develop and test methods for assimilating satellite altimetry and tide gauge sea level data into state-of-the-art ocean models, with a focus on the Baltic Sea region. The overall goal is to improve the accuracy of sea level forecasts and reanalysis by optimally merging observational data with numerical simulations. The candidate will work in an exciting international research context that addresses both fundamental science (sea level variability and climate change impacts) and practical needs (better storm surge and sea level rise predictions). The position offers extensive collaboration opportunities – including potential internships at leading European marine forecasting centers – and a chance to contribute to the Copernicus Marine Service, the European operational ocean monitoring program. This is a fully-funded 4-year PhD where the student will benefit from a multidisciplinary environment, combining oceanography, data science, and AI, while engaging with top researchers across Europe.

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The quantification of volume, heat, and salt fluxes between the ocean (North Sea) and a marginal sea (Baltic Sea) remains one of the most challenging and critical problems in regional oceanography. The dynamics of this exchange govern the salinity, stratification, and deepwater ventilation of the Baltic Sea, with far-reaching implications for its biogeochemistry and ecosystem health. This PhD project will focus on quantifying these fluxes through the narrow and topographically complex Danish Straits (Øresund, Great Belt, Little Belt), combining high-resolution observations and state-of-the-art ocean modeling. The project contributes to ongoing national and international research collaborations and provides opportunities for advanced training in ocean physics, climate impact studies, and numerical modeling.

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The main objective of this research project is to develop an integrated diagnostic system for electrical machines based on custom-designed embedded hardware and edge-deployable AI models. Building upon prior work that established basic IoT connectivity and AI-based fault detection methods, this research will advance toward a deployable industrial solution by developing a custom PCB platform for real-time condition monitoring, on-device inference, and industrial communication. The system will incorporate optimized machine learning algorithms, support standardized IoT protocols, and be validated in laboratory and semi-industrial environments. The project contributes to smart maintenance strategies within the broader context of Industry 4.0 and is part of an ongoing initiative to enable predictive diagnostics for electrical machines.

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The main objective of this project is to develop, test and implement a real-time multicamera system including computer vision algorithms for the detection, classification and tracking of underwater objects. This work will include both living and nonliving biological entities (e.g. fish, woody debris) as well as non-biological objects (e.g. refuse) in order to automate environmental monitoring in shallow (< 30 m) freshwater, brackish and saltwater environments. Specifically, this project addresses three research questions, where the first two questions address technology and knowledge gaps and the third question is designed to guide and inform future works: RQ1) What multicamera configurations are most suitable for computer vision pipelines considering real-time detection, classification and tracking tasks? RQ2) What are the most effective combinations of hardware and algorithms for enabling real-time freshwater fish counting and coastal pollution monitoring, considering power efficiency, size constraints, and computational performance? RQ3) What are the hardware (high performance desktop vs. embedded), software, environmental, human resource and financial requirements to develop automated real-time monitoring systems based on the best available technologies?

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This PhD project focuses on the fabrication of functional materials with enhanced properties for industrial applications using the laser powder-bed fusion (LPBF) process. Particular attention will be paid to alloy design, parameter optimization, and the interplay between microstructure properties and the LPBF technique. Key aspects of the project will include the: (1) Development of novel and next generation functional materials for LPBF process (process-specific design) (2) Optimization of the LPBF process parameters to achieve desired microstructure and material properties, (3) In-depth materials, and property testing and (4) Prototype development and integration of functional materials in real-world applications. Fundamentals of the microstructural development will be studied in detail to fabricate sustainable and high-performance functional materials by the LPBF process.

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This PhD project focuses on advancing circular economy (CE) practices in the engineered wood products and electronics sectors to support EU decarbonization and industrial sustainability goals. The research will develop integrated materials sustainability assessment frameworks using life cycle assessment and circularity indicators. Key questions include how to assess materials’ circularity and redesign value chains for improved resource efficiency and resilience. Embedded within a broader European sustainability initiative, the project offers opportunities for industrial collaboration and applied research. The candidate will help operationalize CE strategies, promoting sustainable product design and supporting companies in adopting circular business models aligned with EU policy targets.

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The digital transformation of education requires innovative approaches to cybersecurity and artificial intelligence integration across all educational levels. This PhD position focuses on developing effective frameworks for implementing cybersecurity education and AI tools in vocational schools, high schools, and undergraduate programs. The research aims to identify current skills gaps, evaluate existing integration models, and create evidence-based strategies to enhance digital competencies among students and educators. This position offers an opportunity to conduct groundbreaking research at the intersection of cybersecurity, artificial intelligence, and education while contributing to the development of next-generation educational frameworks that prepare students for an increasingly digital future.

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The overall goal of the Thesis project is to examine and develop the measurement techniques to characterize statically or dynamically.soft tissues (eg heart muscle, lungs, overall body composition, skin etc) in clinical environment, in co-operation with Tartu University Hospital. Methods under interest could include using of the electrical bio-impedance (EBI) and electrical impedance tomography (EIT), but can consider also using of the magnetic induction and other sensors with related signal processing. Modelling and digital twinning could be considered as a promising option to go beyond the state of the art, while of course inventing and developing of the advanced instrumentation is a great challenge. Also, classical instrumentation challenges – in improved calibration, accuracy, resolution, frequency range – are in place. Also basic image processing and machine learning could complement the developed measurement techniques.

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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 term “phytoplankton” describes an extremely diverse group of mostly single-celled photosynthetic organisms that drift with currents in marine and freshwater. Phytoplankton monitoring, which is essential in assessing the status of aquatic ecosystems, currently mainly employs “classical” optical microscopy-based identification methods. The resulting data are used for ecological status assessments and for the calculation of eutrophication and food web indicators, required by the EU Water Framework and Marine Strategy Framework Directives, as well as for long-term records and other scientific research. Microscope-based identification methods are time-consuming and require increasingly rare high-level taxonomic expertise, which even among highly trained specialists introduces significant levels of observer bias. An alternative method, DNA-based barcoding, provides a cost-effective, relatively unbiased way to increase available information on biodiversity, especially of taxa that are difficult to identify using microscope-based analysis, including small-sized or cryptic taxa, as well as rare species that can provide important knowledge on ecosystem health and biodiversity. This PhD project will focus on advancing and standardizing the use of molecular tools, such as metabarcoding and long-read sequencing, to improve the identification of phytoplankton and biodiversity assessments.

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The PhD project aims to assess the health of marine ecosystems by measuring phytoplankton diversity and following changes in their community over various timescales and sea areas. Phytoplankton communities play a vital role in ecosystem health, and changes in species diversity and succession often signal environmental stressors such as eutrophication and increased sea surface temperatures. Furthermore, there are around 300 phytoplankton species associated with harmful algal blooms that, depending on the causative species, may have different harmful effects, such as mortality of cultured fish and invertebrates or toxin accumulation in the food chain. During recent decades, HAB species distribution, blooms, areas affected by HABs and toxicity events related with HAB species have expanded worldwide and among other factors it has also been linked to eutrophication and global change. During the proposed project, interactions and shifts in phytoplankton communities will be monitored in parallel with various environmental parameters. Phytoplankton community composition will be identified by using both traditional morphology-based identification and environmental DNA-based detection. The data used in this project has been collected over several years from the same locations and the sampling will also continue during the duration of the project to capture changes over longer timescales. The results of this work will provide an insight into variability in the phytoplankton community composition, potential interactions between the species and how physical-chemical parameters drive or contribute to the detected changes. This information will be invaluable for understanding the responses of the phytoplankton community to future changes in the marine environment and the overall functioning of marine ecosystems.

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In this position, you develop an intelligent motion control system for Maritime Autonomous Surface Ships (MASS). Aim of the control systems is to optimize the MASS’s behavior and response in demanding environmental conditions, with the focus on stabilizing the of angular motions and improving the navigational precision and path-following. MASS control is based on the identified wave/environmental conditions and on the feedback loop of ship’s response.

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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 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, in a multiplexed manner.

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This PhD project seeks to turn a problem into an opportunity, by developing and applying Life Cycle Assessment (LCA) tools to (1) minimize waste and (2) enhance sustainability and availability of critical raw materials. This PhD project will contribute to the raw materials industry's sustainability by proposing an innovative supply chain approach for strategic raw materials based on the valorization (recycling and secondary re-use) of industrial wastes. In this work, several processing routes of raw material waste (including overburdens and industrial mineral tailings) will be assessed for recycling into construction materials or chemical products. We will include input from several domestic and international industrial partners, who will provide sectoral expertise and share information about company activities in waste management. The research objective is to identify the environmental impact and sustainability potential of selected valorization routes using LCA, hot spot analysis and risk identification, creating a sustainability indicator. This work will significantly contribute to developing a quantitative decision support system for environmental, social and health impact assessment (ESHIA) aligned with the targets of the UN Sustainable Development Goals (SDGs).

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This position aims to advance the field of automated driving through cutting-edge research in utilizing data-driven techniques with our custom automated shuttle, TalTech iseAuto. The focus is on leveraging knowledge coming from data and how to extend the concepts of dataset and dataspace for automated driving. Applied methodologies include perception, machine learning, and sensor fusion to enhance the reliability, robustness, and accuracy of perception modules in autonomous vehicles.

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The overall goal of the project is to develop AI-driven battery management strategies for V2X-enabled Software-Defined Vehicles (SDVs) to enhance energy efficiency, extend battery lifespan, and optimize power distribution. The project addresses the following research questions: How can artificial intelligence and real-time data analytics be used to predict battery degradation and optimize charging cycles in SDVs? What are the most effective methods for integrating multi-domain battery models into SDV architectures for improved performance and control? How can hybrid AI- and physics-based optimization strategies be designed to support predictive maintenance and adaptive energy management under dynamic driving conditions?

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The PhD project aims to develop energy-efficient droplet biotechnology instrumentation for research and development. The project focuses on flow and thermal regulation, light intensity measurement, system integration, and edgeAI control, with a focus on sustainability and green electronics.

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This PhD project focuses on the empirical validation and intelligent data integration of Electrical Propulsion Drive System (EPDS) components in Software-Defined Vehicles (SDVs). Building upon the foundational modeling and digital twin frameworks developed within the realization of the PRG2532 project, this research will deliver comprehensive testing protocols, advanced data pre-processing strategies, and condition monitoring solutions for EPDS subsystems.

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The goal of this research is to enhance the safety and efficiency of automated vehicles by developing a collaborative multimodal perception system. This system integrates data from various sources, such as cameras, LiDAR, radar, and V2X communication, to create a comprehensive understanding of the vehicle's surroundings. The research will be run in the Autonomous Vehicles research group within the TalTech facility and using the innovative iseAuto shuttle v.2.0. By sharing perception data with other vehicles and infrastructure. The research aims to improve the detection of complex environments, anticipate hazards, reduce accidents, and optimize traffic flow, ultimately enabling safer and smarter autonomous driving.

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This PhD project aims to advance the modeling of Electrical Propulsion Drive System (EPDS) in Software-Defined Vehicles (SDVs) by leveraging hybrid, multi-domain, and multiscale modeling approaches. The project’s objective is to develop a unified framework that integrates physics-based and data-driven methodologies to enhance the modeling fidelity of EPDS, bridging the gap between component-level and system-level perspectives. This research addresses the need for more comprehensive and computationally efficient models that account for the interactions between electrical, mechanical, electrochemical, and thermodynamic domains.

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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 Supervisor: Prof. Artu Ellmann Co-supervisor: Prof. Nicole Delpeche-Ellmann Research group of Geodesy and Road Engineering, Dept. of Civil Engineering and Architecture

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This PhD research project will focus on designing and synthesizing suitable first row transition metal complexes capable of binding H2 in reversible manner to carry out hydrogenation reactions. While doing so, the successful candidate will dwell into the world of organometallic chemistry to design, synthesize and utilize custom made ligands on the selected non-precious metal salts. These complexes would then be assessed in hydrogenation reactions to evaluate their para-hydrogen induced polarization transfer efficiency. This fully funded PhD project will offer the candidate an opportunity to learn the skills necessary to carry out air- and moisture sensitive chemistry using organometallic complexes, and secondly it will expand the application of these complexes into solution NMR to increase the signals of low concentration analytes in a mixture of interest. The overall goal of the project is to test and find the structurally simplest non-precious metal complex capable of inducing NMR signal enhancement in presence of para-hydrogen (p-H2) and suitable selection of substrates. The project addresses the following research questions: Which non-precious metal based system is suitable for redox chemistry application? Which types of ligands have an important effect of the reversible binding of H2? How different types of substrates of interest interact with these metal complexes?

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The Estonian Maritime Academy, TalTech is inviting applications for a fully funded PhD position in the field of Artificial Intelligence (AI) applied to Cumulative Impact Assessment. The position is part of an interdisciplinary research initiative aimed at developing AI-driven methodologies to extract and analyze impact information from scientific publications, with a particular focus on environmental and marine sciences.

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The global push towards sustainable development and energy efficiency has led to an increasing interest in flexible energy technologies in buildings. This PhD research aims to develop a robust methodology for the financial and technical assessment of energy flexibility in buildings, addressing the key barriers to implementation. The transition towards energy-efficient and flexible buildings is important in addressing climate changes and optimizing energy usage in urban environments. Energy flexibility in buildings refers to the ability of a building to adjust its energy consumption in response to external conditions, such as energy prices, grid demand, and renewable energy availability. The need for a systematic and data-driven methodology to assess the financial and technical viability of energy flexibility solutions has become increasingly relevant, particularly in smart grids and sustainable urban planning.

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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 main objective of this research project is to develop methods to assess the actual condition of substation instrument transformers using modern power system monitoring data, e.g., PMUs, PQ meters, TFR, etc. It is of interest to determine which of the available measurement data and how can be used considering the data availability and cost limitations. Within the research it is possible to participate in activities performed in high-voltage laboratory and using RTDS. The research is financed by Estonian TSO. This research project is part of the wider project which objective is to develop methods to assess the condition of Transmission Network substations and determine the optimal approach for equipment maintenance principles.

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