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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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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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. Empa is a research institution of the ETH Domain. Empa's Laboratory of Biomimetic Membranes and Textiles is a pioneer in physics-based modeling at multiple scales. We bridge the virtual to the real world
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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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CoSi project (Co-Evolution and Coordinated Simulation of the Swiss Energy System and Swiss Society), we are seeking a motivated and qualified PhD candidate to develop neighbourhood- and district-scale
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. Empa is a research institution of the ETH Domain. Empa's Laboratory of Biomimetic Membranes and Textiles is a pioneer in physics-based modeling at multiple scales. We bridge the virtual to the real world