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sensing systems Design and validate machine learning models for predictive monitoring of physiological states Analyse large experimental datasets and quantify sensor performance (accuracy, robustness
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to staff position within a Research Infrastructure? No Offer Description PhD Position in Physics-Informed Machine Learning for Cardiac Magnetic Resonance The CMR Zurich group at the Institute
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at the interface of machine learning, statistics, and live-cell biology. The position is co-supervised by Prof. Olivier Pertz (Cell Biology) and Prof. David Ginsbourger (Statistics), and the student will be equally
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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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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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flow reconstruction, enabling both real-time coarse diagnostics and high-fidelity offline velocity field estimation. Developing reinforcement learning (RL) algorithms for a multi-agent robotics system
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opportunities and value diversity and respect in our working and learning environment. We are seeking a highly motivated doctoral researcher to investigate how metabolic programs enable cancer cells
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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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(SNSF) within the project “Learning the activation mechanism in G protein-coupled receptors from free energy calculations.” The research will focus on the activation mechanisms of G protein-coupled
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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).