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of material behavior to the development of the material to the finished component. PhD position on physics-based machine learning modeling for materials and process design Reference code: 980 - 2026/WD 1
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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
., using FEniCSx) Advanced knowledge of scientific programming, preferably in Python, including experience with implementing machine‑learning methods (e.g., PyTorch) Excellent spoken and written English, as
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plate array microscope for simultaneous time-lapse video microscopy, enabling high-throughput single-cell analyses of rapidly migrating cells. You will be responsible for Developing new machine learning
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experience Practical experience in machine learning and the application of large language models Knowledge of OMICS and image data analysis A willingness to engage in interdisciplinary scientific work
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of artificial intelligence, machine learning and/or deep learning experience in scientific publishing and presenting research results knowledge or experience in public health research Personal skills Independence
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research environment for biophysics. Our group combines molecular dynamics simulations with machine learning techniques to understand how proteins, biomembranes, and small drug-like molecules interact
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research group “Machine Learning for Biomedical Data” led by Prof. Dominik Heider and is embedded in the DFG-funded Collaborative Research Centre 1748, Principles of Reproduction. The CRC 1748 involves