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dynamics simulations is highly desirable. Basic knowledge of machine learning is considered an advantage but is not mandatory. LanguagesENGLISHLevelExcellent Additional Information Work Location(s) Number
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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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systems, and space applications. We combine theory, physics-based simulations, machine learning, and autonomous workflows to understand and design materials that can perform under conditions where
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combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real-world energy applications, the project aims to better capture the dynamics of urban infrastructures
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with competitive salary according to ETH standards Interdisciplinary and international research environment You can expect numerous benefits , such as public transport season tickets and car sharing, a
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the project, supported by Dr. Adamo and close collaboration partners, within an environment that encourages academic freedom and scientific independence. In line with our and Uni Basel values (https
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transfer, developing and employing computer simulations, laboratory experiments, and field analyses. Our aim is to gain fundamental insights and develop sustainable technologies to address societal needs
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the project, supported by Dr. Adamo and close collaboration partners, within an environment that encourages academic freedom and scientific independence. In line with our and Uni Basel values (https
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, supported by Dr. Adamo and close collaboration partners, within an environment that encourages academic freedom and scientific independence. In line with our and Uni Basel values (https://www.unibas.ch/en