74 computational-physics "https:" "https:" "https:" "https:" "Simons Foundation" PhD positions at Technical University of Munich in Germany
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12.11.2025, Wissenschaftliches Personal Join our team in a collaboration between TUM and Politecnico di Milano! Develop cutting-edge methods combining remote sensing, physics-based modeling, and
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or comparable degree in physics, biology, bioengineering, material engineering or a related discipline. You have experimental experience in cell/tissue culture, microfluidics, or a related discipline. You enjoy
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Profile The ideal applicant has a strong background in bioinformatics or computational chemistry, as well as data analysis and solid English-language skills. Experience with programming is highly
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in the deep ocean and help unravel how life emerged on earth. Requirements As a suitable candidate, you have an outstanding Master's degree or comparable degree in physics or a related discipline. You
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in biology, physics or a related discipline. You have experience in quantitative biology, experimental soft matter, or experimental biophysics. You enjoy working in interdisciplinary and international
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part of the PhD research experience and are explicitly encouraged (e.g. South America, Asia or Africa). The PhD process will be accompanied by integration into TUM’s School of Life Sciences or School
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integral part of the PhD research experience and are explicitly encouraged (e.g. South America, Asia or Africa). The PhD process will be accompanied by integration into TUM’s School of Life Sciences
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of helicopter components using a data-based as well as a physics-based approach. In the project “BIG-ROHU” (BIG Data - Rotor Health and Usage Monitoring), a system is being developed in cooperation with Kopter
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Universitätsklinikum rechts der Isar der TU München Ismaninger Str. 22, 81675 München http://kornlab.med.tum.de The position is suitable for disabled persons. Disabled applicants will be given preference in case
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07.04.2026, Academic staff PhD position at the interface of computational physics, machine learning, and experimental reactor design. The project focuses on developing PINN-based simulations