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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
, Engineering Physics, Physics, Mathematics, or a related field Salary group E13 TVöD Temporary contract untilfor 24 month Full-time / suitable as part-time employment The Bundesanstalt für Materialforschung und
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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application. Qualifications and specific competences Applicants must hold a relevant Master’s degree in Bioinformatics, Computational Biology, Computer Science, Mathematics, Physics, or a related field and
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mathematical modelling and simulation. Partial differential equations will be applied to model drug distribution and partitioning in multiphase systems using the drug’s partition coefficient as a driving force
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) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission of teaching and
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The Department of Mathematics and Computer Science at the University of Southern Denmark (Odense) invites applications for a PhD scholarship in computer science under the umbrella of the Danish
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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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[GEM] Coordinator: prof. Brunella Franco Info: gem[at]ssmeridionale.it Mathematical and physical sciences for advanced materials and technologies [MPHS] Coordinator: prof. Nicola Fusco Info: mphs
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) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission of teaching and
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science, applied mathematics, physics or a similar area very good programming skills in Python good prior experience with neural networks using common Python-ML libraries such as PyTorch background knowledge in