32 computational-physics "https:" "https:" "https:" "https:" "Grenoble INP Institute of Engineering" PhD positions in Luxembourg
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character. The Faculty of Science, Technology and Medicine (FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer
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character. The Faculty of Science, Technology and Medicine (FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer
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through the EU Research Framework Programme? Other EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you passionate about research? So are we! Come
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citizens in their choices, public authorities in their decisions and businesses in their strategies. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute
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2027 - 02:53 (UTC) Country Luxembourg Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to
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Mathematics, Physics, Computer Science, Engineering or another quantitative subject are particularly encouraged to apply. Language Requirements: Applicants must demonstrate at least B2-level proficiency in
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. The group consists of doctoral and post-doctoral researchers from diverse backgrounds. For more information, please visit our website: https://wwwen.uni.lu/snt/research/finatrax/projects Successful candidate
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2027 - 01:48 (UTC) Country Luxembourg Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to
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! Education · PhD in computer science, engineering, applied mathematics, physics, or another STEM discipline. · Demonstrated experience with mathematical and numerical optimization methods
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research program that brings together physics, chemistry, and machine learning. Your research tasks will include: Uncertainty Estimation in Deep Neural Networks for MLFFs Implement and test uncertainty-aware