9 postdoctoral-mathematics PhD positions at Chalmers University of Technology in Sweden
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PhD Position in Theoretical Machine Learning – Understanding Transformers through Information Theory
. This PhD project aims to close that gap by developing a rigorous mathematical framework to analyze and explain transformers using information-theoretic tools. You will be co-supervised by: Research
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240 higher education credits in Applied Mathematics, Applied Physics, Electrical Engineering, Mechanical Engineering, or a related field. A strong mathematical foundation and excellent academic
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We are looking for a highly motivated, skilled, and persistent PhD student with experience in computational fluid dynamics (CFD) and some knowledge in structural analysis. The research aims to investigate flow-induced forces in hydraulic turbines under varying operational conditions and how...
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technology, or applied mathematics, with a specialization in energy systems analysis, or energy economics. Strong written and verbal communication skills in English. The following experience will strengthen
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What does policy and practice for recruitment to university STEM (Science, Technology, Engineering, and Mathematics) education look like, and what are the consequences for equality and social
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the form of academic publications and presentations 20% of teaching for the department Read more about doctoral studies at Chalmers here . Qualifications Strong interest in the mathematical analysis
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: M.Sc. degree (or equivalent) in Physics, Mathematics, Computer Science, or a related field, corresponding to at least 240 higher education credits. Proficiency in conducting literature reviews. Expertise
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of about 10 percent of full time. Qualifications The applicant should have a Master's degree in Applied Mathematics, Engineering Physics, Electrical Engineering, or equivalent, with excellent knowledge
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. The applicant should have strong background in mathematical foundations of computer science and experience in Python programming. Previous experience in deep learning, reinforcement learning, or explainable AI is