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Underwater critical infrastructure, such as sub-sea cables and pipelines, plays an essential role in global communications, energy transmission, and maritime operations. This infrastructure spans long distances, yet is highly vulnerable, as demonstrated by recent incidents. These incidents pose significant risks to economic stability, environmental safety, and national security. The inherent complexity of underwater environments, combined with the vast, often inaccessible nature of these infrastructures, makes real-time tracking, surveillance, and protection a formidable challenge. The underwater environment presents numerous challenges due to harsh conditions and limited access to energy, communication, and maintenance.

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In this industrial PhD sponsored by Estonian firm DefSecIntel, we will explore, develop, test and validate new data assimilation methods for integrated autonomous situation awareness urgently needed for monitoring and surveillance systems. Combining sensor data time series with digital imagery remains theoretically challenging as the spatial and temporal scales, measurement sensitivities, detection range and noise from each type of sensing system typically have little overlap. A promising method to address these challenges is data assimilation, which has strong theoretical underpinnings based on optimal filters and is commonly used to update large-scale numerical model forecasts in the geosciences. Outside of this largely specialist academic community however, data assimilation remains underutilized as a powerful tool for combining sensor time series measurements with grid-based data such as digital imagery and numerical model results. To address this lack of suitable methods for a wider number of use-cases, the TalTech Environmental Sensing and Intelligence group has pioneered the development of lightweight data assimilation algorithms capable of estimating unknown uncertainty, and which can be deployed on real-time systems based on microcontrollers.

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This PhD position focuses on the research and development of novel algorithms for autonomous marine navigation. The candidate will investigate approaches to enable safe and efficient navigation for autonomous ships, particularly in complex and high-traffic environments such as harbors, while ensuring compliance with COLREG (International Regulations for Preventing Collisions at Sea). The research will explore real-time path-planning techniques that involve navigation algorithms, for example, artificial potential fields, RRT*, and hybrid A*-like methods, etc., combined with formal modeling approaches or neural networks for collision prediction and threat assessment. A key challenge lies in designing algorithms that adhere to COLREG rules while also guaranteeing safe navigation in scenarios where other vessels fail to follow these regulations. The outcomes of this research will contribute to the development of robust navigation algorithms, empowering autonomous ships to operate safely and reliably in dynamic and unpredictable marine environments.

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There are many studies about crisis management, but much development is still to come in the context of e-governance and e-services. In different possible crises, the crucial key issue is to get quick overview of the region to make right decision and activate crisis' action plans to save lives. Countries must be ready for different threats and key issue is to find right ways how to conduct ICT and e-services development with crises management. This research problem is to find a better e-solution for apartment associations, local municipalities, and governmental organizations to prepare for different crises, using modern technology, available e-services, and databases to develop the readiness and improve cooperation. Connecting community and social activism with governmental e-services have a vast potential. The main problem to solve is to offer new e-service to get an overview of all the apartment associations in the district, and their needs, mapping all the vulnerable people in the district together with categorizing what are their living conditions and needs in the possible crises, getting direct contact with people, and together planning crisis plans and developments. Key point is close linkages with local communities using e-services. The objective of this doctoral dissertation is to consolidate and systematize the best practices in crisis response and preparedness, and to develop a comprehensive e-service model that enhances readiness and ensures optimal coordination during crises. This model aims to safeguard human lives, protect assets, and facilitate successful recovery and resilience in the aftermath of crises.

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This topic will focus on enhancing the formation of CSA clinker minerals synthesized from natural raw materials and industrial waste products. Aim is to enhance the reactivity and hydraulic activation by other mineral compounds in different aluminate blends, which is the key in developing new low energy and low GHG based cement production.

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

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

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The proposed topic focuses on bridging the gap in current stimulus-responsive luminescence mechanism by utilizing the thermo-mechano-opto effect of ceramics. Given the abundant transitions and the stress-responsive ability of ferro and piezoelectric ceramics, multi-activator-doped ferro/piezoelectric materials are promising candidates for multifunctional detection based on luminescence characteristics. The project aims at fabricating translucent and /or transparent ceramics and tandem designs combined with ferroelectric and luminescent materials. The multidimensional scheme will use the intensity, wavelength and bi-temporal multiplexing.

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

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Infertility affects about every 6th couple worldwide, but its aetiologies vary greatly. Ovarian dysfunction may lead to anovulation, inadequate steroid biosynthesis as well as problems with oocyte meiotic and cytoplasmic maturation causing female infertility. By studying the ovarian somatic cells of in vitro fertilization patients and their treatment outcomes, we can pinpoint the variable molecular aspects of ovarian infertility that in the future lead to better medical options for these women.

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The research will be conducted in the framework of the research project “Data-driven assessment of the potential and impact of energy saving flexibility technologies in buildings” in cooperation with the company R8 Technologies (https://r8tech.io).

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Hyping Agriculture and Transit (HAT) in 15-minute Cities (15mC) – Food-growing public transport-oriented communities driving urban transitions as green Proximity Oriented Developments (PODs)

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Hyping Agriculture and Transit (HAT) in 15-minute Cities (15mC) – Food-growing public transport-oriented communities driving urban transitions as green Proximity Oriented Developments (PODs)

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

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The overall goal of the project is to examine the changing role of digital competences of healthcare workforce from micro and macro-levels. The project addresses the following research questions: Which clinical competences are currently expected from the healthcare workforce globally and in Estonia? How is the governmental initiative influencing change in digital competence education in Estonia? Which strategies are effective in teaching digital competences as continuous education and in the implementation of daily clinical practice?

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The core objective of this doctoral research is to develop and refine advanced methods for modeling and controlling complex dynamic processes in industrial environments, with a particular focus on enhancing the capabilities of the FOMCON (“Fractional-Order Modeling and CONtrol”) toolbox towards achieving these goals. This entails investigating both theoretical and applied aspects of fractional-order calculus, nonlinear system identification, and distributed control strategies, as well as ensuring that these innovations can be seamlessly integrated into industrial workflows with the help of FOMCON toolbox.

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This PhD position focuses on advancing the field of autonomous marine navigation in GNSS-denied environments. During the PhD, the candidate will investigate methods to localize autonomous vessels in areas where GNSS is either unavailable or unreliable. The research will explore localization techniques such as visual odometry using sea bottom images acquired from side-scan sonar, as well as sensor fusion involving visual odometry data, water flow sensors, and inertial measurement units (IMUs). The project will also involve addressing challenges related to noise reduction from environmental factors such as waves and other disturbances. The outcomes aim to enhance precise navigation of autonomous vessels in GNSS-degraded conditions.

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

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This PhD position, part of the UrbanSplash project within 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 streams, 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.

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The combination of stochastic machine learning and experimental work in the design of ionic liquids (ILs) and deep eutectic solvents (DESs) for latent heat thermal energy storage (LHTES) represents an innovative approach to materials science. This interdisciplinary research not only advances our understanding of electrolyte fluids but also contributes to the development of more efficient and sustainable energy storage solutions. Therefore, the results of this research have the potential to impact several sectors, including renewable energy, industrial processes, and beyond. The aim of the PhD project is to use available datasets to develop and apply stochastic machine learning models to predict the properties of ILs and DESs, followed by the design and synthesis of the most promising new ILs and DESs based on the model predictions. The relevant physicochemical properties of the synthesised materials will be measured experimentally to validate the modelling results. The PhD project is co-supervised by Oliver Järvik (Department of Energy Technology, Tallinn University of Technology, Estonia) and Dinis Abranches (CICECO - Aveiro Institute of Materials, University of Aveiro, Portugal).

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

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For over a century, drawing and writing tests have been integral in the fields of psychology, neurology, and psychiatry. The advent of digital tablets, followed by tablet PCs, has paved the way for the digitisation of these tests, significantly expanding their potential. By integrating statistical machine learning, and more recently, deep learning techniques, the ability to analyse fine motor tests has greatly improved, offering sophisticated insights that support the diagnosis and monitoring of Parkinson’s disease and other cognitive disorders. However, the application of AI in the medical field presents unique challenges, especially regarding the transparency of AI-driven decisions. In such a delicate area as healthcare, it is imperative that AI models can be understood and trusted by medical professionals. Transparency is often achieved through the addition of so-called ‘explainers’ or ‘interpreters’ as a post-hoc step in the workflow. These tools typically highlight the importance and location of features that influence the decision-making process, often pointing out the most relevant parts of images or data. Despite these advances, current AI workflows may still produce feature sets that are not entirely comprehensible to healthcare practitioners. The complexity of the models can make it difficult for clinicians to interpret results in a meaningful way. This research aims to bridge that gap by prioritising explainable AI capabilities in the development of workflows that are designed specifically for medical practitioners. The goal is to create AI-driven diagnostic tools that are not only accurate but also transparent, providing clear insights that enhance decision-making in cognitive disorder diagnostics.

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Cognitive buildings represent a new era of structures that can react to surroundings and adapt to the needs of their occupants. This paradigm is steered by a mixture of enabling technologies such as AI, ML, IoT, data science, and a human-centric approach. The latter ensures that buildings augment the quality of life, highlighting a new era in the relationship between inhabitants and their physical environment. The amount and complexity of data in buildings makes them ideal candidates for sophisticated ML models. However, the frequent problem is the lack of interpretability of such models, meaning we do not understand how they make their decisions. Establishing trust between occupants and cognitive buildings becomes essential, as a trusted environment can lead to greater acceptance and appreciation of these advanced capabilities. eXplainable Artificial Intelligence (XAI) is a concept that tries to close this gap.

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

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This PhD project focuses on sustainable business pathways and innovation activities in turbulent environment in the framework of a project “Just transition governance models and entrepreneurship pathways: monitoring and analyses" (01.01.2024−31.12.2028), work-package “Ida-Viru changing business models”, which is funded by the European Just Trust Fund and is carried out in close cooperation between TalTech and University of Tartu in Estonia. The overall ambition of the Just Trust Fund research activities is to achieve a fundamental improvement of the Ida-Viru County region's economic, environmental, social, living and business conditions, and to develop a theoretically grounded framework for transition processes as well as methodology and model(s) for empirical monitoring of relevant aspects of transition. In the context of large societal transformations, namely transitioning to a climate-neutral economy, it is essential to restructure the economy of Ida-Virumaa and achieve sustainability, as well as to identify a new development trajectory. This represents a core theme of economic geography in the context of regional development (Sotarauta 2020). The changing economic environment, alongside new technologies (i.e., renewable energy solutions), influences governance, internal management models of businesses, their value propositions, supply chains, and the entrepreneurial ecosystem, as well as the daily lives of local people and communities. At the core of transitioning to a climate-neutral economic model is the identification and implementation of new business opportunities. This involves business model innovation which means creating and applying new business models, diversifying operations by adding additional models, adopting new models, or replacing an existing model with another (Geissdoerfer et al. 2018). It is influenced by micro (knowledge and skills, previous experiences, mindset and orientation, values), meso (networks, economic sector, finances, human capital, innovation culture, technology) and macro level (entrepreneurial ecosystem, geography, societal transformations) factors (ETC/WMGE Report 2/2021; Jacobsson & Bergek, 2011). The aim of the PhD project is to study with mixed methods (qualitative and quantitative) found mainly in business research, the factors that influence sustainable business development pathways at micro, meso and macro levels, incl. business model innovation, its drivers and barriers, with the overall aim to develop a methodology for business model innovation analysis and typology of business model innovations to contribute to the sustainable transition of Ida-Viru County in Estonia. The consideration of comparative aspects, for example, with other Estonian and EU regions (preferably other just transition regions), is possible. The project addresses research questions as follows: What are the (sustainable) innovation-related activities Ida-Viru businesses have implemented and plan to implement (product and process innovation)? What are the drivers and barriers at various levels (knowledge, technology, financial resources, market and customers, supply chains, legislation, productivity, and R&D intensity, networks, etc) and how these have influenced the changes in Ida-Viru business models? Based on the innovation activities and their influencing factors identified in the study region, which business model innovation typology emerges as relevant and applicable in the context of big societal transformations?

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

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The 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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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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Cellulose, as the most abundant, naturally occurring biopolymer in the world, is an important resource for replacing fossil-based plastics, as it has good mechanical properties and chemical durability, it is nontoxic and is not competing food resources. However, only minor amount of global plastic production is covered by cellulose derivatives or regenerates up to now. This is pointing strong need to increase utilisation of this sustainable, carbon neutral raw material for plastics. Unlike most of the commodity plastics, cellulose is not intrinsically thermoplastic and must be chemically modified to achieve melting behaviour, expected by plastics processing industry. The cellulose modification methods known so far are resource- and energy-intensive. This stimulates development of more sustainable routes. The modern society, from other hand is suffering due to intensive use of fossil-based plastics causing littering and pollution in soils, sediment and water, contamination of food and living tissues with plastic particles and leeched additives. Cellulose based bioplastics are sustainable solution for this problem. Therefore, the study is devising and demonstrating novel, sustainable esterification routes for preparing thermoplastic cellulose esters and their potential application in several commodity and high-tech applications.

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

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