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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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by multi-parameter sensing and creating digital twins of heat-sensitive biological systems (food, humans) that can live together with their real-world counterparts. This project aims to identify better
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in physics-based modeling at multiple scales. We bridge the virtual to the real world by multi-parameter sensing and creating digital twins of heat-sensitive biological systems (food, humans) that can
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topological cell guidance cues and multi-scale directional porosity. The laboratory is embedded in ETH Zurich’s Department of Health Sciences & Technology (D-HEST). D-HEST is a stimulating environment for
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bring interdisciplinary expertise on energy transitions, with a solid understanding of renewable energy integration, multi-energy systems, and energy conversion and storage technologies. You have strong
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understanding of district heating and cooling, renewable energy integration, multi-energy systems, and energy conversion and storage technologies. You have strong skills in programming, modelling, and data
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by multi-parameter sensing and creating digital twins of heat-sensitive biological systems (food, humans) that can live together with their real-world counterparts. This project aims to better identify