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contribute to the development of innovative, physiology/ machine learning-driven clinical solutions and decision support tools for critically ill patients, focusing on cardiovascular and respiratory monitoring
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Electrical Engineering, Computer Science, or a related discipline. A research-oriented attitude. Solid background in machine learning and optimization methods. Knowledge and experience in (wireless
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(HIMS), in close collaboration with industrial partner BOR-LYTE and Smart Industry testbeds. This position offers a unique opportunity to combine inorganic chemistry, spectroscopy, machine learning, and
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Intelligence, Applied Mathematics, Electrical Engineering, or a closely related field. You have demonstrated expertise in machine learning and deep learning, with experience in time series forecasting or related
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The position is part time, 20 hours per week for up to 20 months. The salary scale is 10.0. Information and application To learn more about the project, you may visit: https://www.utwente.nl/en/bms/pa/research
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To learn more about the project, you may visit: https://www.utwente.nl/en/bms/pa/research/bridge/#project-team For inquiries, please contact Dr. Le Anh Long l.a.n.long@utwente.nl About the organisation
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and Liu, Supervised learning in physical networks: From machine learning to learning machines, PRX 11, 021045 (2021) [2] Stern and Murugan, Learning without neurons in physical systems, Ann Rev Cond
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You hold a PhD in Computer Science, Artificial Intelligence, Applied Mathematics, Electrical Engineering, or a closely related field. You have demonstrated expertise in machine learning and deep
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degree in AI, Computing Science, Mathematics, or Data Science. Strong coding, communication and organizational skills. Demonstrable experience with using machine learning packages (e.g., PyTorch
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methods for evaluating intelligence in people are not suitable for AI and vice versa due to inherent differences in learning, memory, and processing between these systems. This project develops