65 machine-learning "https:" "https:" "https:" "https:" "https:" "University of St" "St" "St" PhD positions in Switzerland
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Baumann (fabian.baumann@unibas.ch ). You can also find out more about us at https://dg.philhist.unibas.ch/de/ . Where to apply Website https://academicpositions.com/ad/university-of-basel/2026/phd-position
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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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-structure interactions of flapping flags (Bio-inspired) unsteady vortex formation and interaction More information about the lab and the ongoing and past projects can be found here: https://www.epfl.ch/labs
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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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predict protein-protein complementarity, design artificial protein binders, investigate the effects of mutations on protein structure and function, and apply protein representation learning to uncover
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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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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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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