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are seeking a motivated PhD student to join our team working on realizing learning in novel physical materials, as part of a joint theoretical/experimental research project between AMOLF and the University
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, Reasoning and Validation (Serval) research group and work on a research project related to the application of machine learning for official statistics. The subject of the thesis will be “Exploring Large
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24 Sep 2025 Job Information Organisation/Company Universität Heidelberg Department Institut für Theoretische Physik Research Field Physics » Other Computer science Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Germany Application Deadline 7 Oct 2025 - 23:59...
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professional development opportunities and strive to meet each individual’s development and well-being goals as much as possible. As an associate researcher with expertise in the field of machine learning within
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Cybernetics at NTNU is offering a fully funded PhD position in the area of learning-based control and decision-making for complex multi-agent systems. The project explores new computational frameworks
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19th October 2025 Languages English English English The Department of Engineering Cybernetics has a vacancy for a PhD Candidate in Learning-Based Control Apply for this job See advertisement This is
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join us as a PhD candidate. You will work in a highly interdisciplinary group, at the intersection of physics, machine learning and theoretical neuroscience. Our group is focused on investigating
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candidate. You will work in a highly interdisciplinary group, at the intersection of physics, machine learning and theoretical neuroscience. Our group is focused on investigating dynamics and learning in
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Are you interested in writing to learn and learning to write in the age of AI? As a PhD candidate, you will investigate these topics at the intersection of (foreign) language learning and
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Candidate Human-Centered Interpretable Machine Learning (1.0fte) Project description In recent years, practitioners and researchers have realized that predictions made by machine learning models should be