62 machine-learning-"https:" "https:" "https:" "https:" "https:" "University of St" "St" PhD positions in Switzerland
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to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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collaboration with PD Dr. Isabel Hostettler (HOCH Health Ostschweiz). The candidate will perform research at Empa in St. Gallen with joint affiliation to ETH Zürich (D-HEST, Prof. Peter Wick). Desired starting
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library. Strong interest in machine learning, reinforcement learning, and fluid dynamics. Ability to work independently and collaboratively in an interdisciplinary team. Excellent command of English, both
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quality journals Personal and professional development support You will be based at Empa in St. Gallen (Claudia Som, principal supervisor, Dr. Roland Hischier and Prof. Dr. Bernd Nowack, co-supervisors) and
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Laboratory is located in St. Gallen at the Swiss Federal Laboratories for Materials Science and Technology (Empa). Our team is highly interdisciplinary and international, providing an inspiring environment
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of novel physics-guided AI algorithms for drug design, integrating physics-based modeling with state-of-the-art deep learning methods. The project will focus on creating a next-generation docking framework
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, statistical physics and quantum solid state physics is absolutely essential. Good computer skills are a plus. For further information see our group web page www.neutron.ethz.ch/. Applications should contain a
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topics. You collaborate on research and pedagogical projects and assist in academic administration to some degree. You will teach two hours per week (during the semester).
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interdisciplinary team including computer scientists, experimentalists and clinicians. ■ You will be involved in cross-disciplinary collaborations and have training opportunities to further develop and grow your