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of Novel Deep Learning Architectures for Fine Motor Test Analysis" Supervisor: Tenured Associate Professor Sven Nõmm Abstract This research initiative focuses on digitizing and analyzing fine motor tests
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recycled sources. Description Within this thesis, the PhD candidate will learn in depth about hard magnetic materials and obtain practical skills, which include: operating two different AM manufacturing
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surface vessels. Using state-of-the-art deep learning models together with various environment sensing methods, the candidate is required to devise novel solutions that can help interpret the environment
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facilities, including modern laboratories and technology-driven learning spaces. You will have access to modern equipment, licensed software, and resources that facilitate learning, experimentation, and
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years a clear interest in the topic of the position principal understanding of electric power systems and a strong background in AI, machine learning strong programming skills (e.g., Python, MATLAB
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degree in Electrical engineering from last 3-5 years a clear interest in the topic of the position profound knowledge of electric power systems, wide-area monitoring and machine learning methods. excellent
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(OriginLab Corporation) Knowledge of electrochemistry Knowledge in the machine learning Specific Requirements The information for the PhD admission is available at TalTech´s web-page: https://taltech.ee/en/phd
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-analytical or numerical model including the description of the environmental loads and structural response, or in (ii) a machine-learning model, where experimental or numerical simulation data is used
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an international team Preferred: Experience in programming and deep learning, showcased through GitHub projects Applicants are encouraged to submit preliminary research plan Specific Requirements The information
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for better insights and decision-making during data collection campaigns. This project gives a great opportunity to dive into the world of mathematical models, machine learning, multimodal sensing, and data