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

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

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

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

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

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

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

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The aim of the project is to implement classical organocatalytic approaches with borane catalysis, thereby unlocking novel reactivity that can revolutionize the synthesis of chiral amino alcohols and diamines. These motifs are widely represented in the structure of bioactive molecules and hold immense potential for applications in pharmaceuticals and beyond. As direct access to these motifs in an enantioselective manner is rare, a strategy enabling their synthesis in an atom-efficient approach would be highly valuable to the chemical community.

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