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machine learning and AI, probabilistic risk modelling, hydrology and actuarial science. We realize that candidates will usually have expertise in one of these fields and ask for a genuine interest in the
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self-assembly of ligand building blocks will be generated and explored. Detailed kinetic data gathered during these studies will also contribute to machine learning (ML) approaches in collaboration with
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processes that span behavioral, cognitive, psychological, and physiological dimensions. To date, these dimensions have largely been studied in isolation, leaving their interconnections and their role in real
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adaptive resource allocation, AI-driven network orchestration, dynamic beam steering, joint communication and sensing, and cross-layer optimization. Machine learning techniques will be investigated
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10 Jul 2026 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Engineering » Computer engineering Engineering » Electrical engineering Researcher Profile
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strong affinity for language data; solid programming skills (e.g., Python) and experience with machine learning or NLP, ideally including transformer-based models and word embeddings; excellent English
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research combines inverse problems, numerical mathematics, optimisation, machine learning and imaging physics, with applications ranging from medical and industrial imaging to geophysics. For more
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that your background does not perfectly match the project. Please don’t let that discourage you. Very few PhD candidates start with all the knowledge they will eventually need. Curiosity, motivation, and a
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of flexible energy resources (e.g., heat pumps); knowledge of mathematical modelling, optimization, machine learning, or data analytics (experience in one or more is desirable); strong scientific programming
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PhD candidate you will develop new ways to extract cosmic-ray physics from KM3NeT data. You will design and characterise reconstruction methods—both machine-learning-based and traditional