23 machine-learning "https:" "https:" "https:" "https:" "The Open University" positions at University of Tübingen in Germany
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11.12.2025 Application deadline: 15.02.2026 The Faculty of Science at Tübingen University invites applications for a W3-Professorship in Machine Learning in Physics at the Department of Physics (m/f
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the organizational activities and implementation of the Cluster’s goals. Further information about the Cluster can be found at https://uni-tuebingen.de/en/research/core-research/cluster-of-excellence-machine-learning
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of molecular and biological matter using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts
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of molecular and biological matter using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts
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and others) Analysis of the experimental data, ideally connecting to our machine learning tools Presentation of scientific results on conferences and in publications Requirements PhD degree in physics
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Astrophysics (IAAT), University of Tübingen, D-72076 Tübingen, Germany https://www.tat.physik.uni-tuebingen.de/~kokkotas E-Mail: kostas.kokkotasspam prevention@uni-tuebingen.de Further information
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machine learning tools for the efficient analysis of the experimental data. For more information, visit our web page www.soft-matter.uni-tuebingen.de We are looking for a motivated PhD student to contribute
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machine learning tools for the efficient analysis of the experimental data. For more information, visit our web page www.soft-matter.uni-tuebingen.de We are looking for a motivated PhD student to contribute
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programme, please see https://uni-tuebingen.de/en/faculties/faculty-of-science/doctoral-studies/ and https://www.phd.tuebingen.mpg.de/imprs? Assessment Submitted applications will be reviewed by IMPRS
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27.10.2025 Application deadline: 30.11.2025 Are you excited about the possibility to explore ethical, philosophical, legal, epistemic or social implications of using machine learning in different