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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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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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anthropology and social science to biostatistics and mathematical modelling as well as observational cohorts with biobanks. The Environmental Exposures and Health Unit (EEH) of EPH is focused on research related
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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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is therefore expect to have experience in at least one of these interdisciplinary areas: 1. Neuroscience and/or VLSI basic device physics. 2. Spike-based computing paradigms: theory and modeling. 3
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of systems. We develop methods to advance sustainable energy and chemical process systems from the molecular to the scale of the systems. Our work combines process analysis and system modeling to design
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near-instantaneous proliferation of comb lines and new regimes of spectral control. Project background This project will combine advanced numerical modeling with laboratory demonstrations to explore
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scanning probe instrumentation fabrication of van der Waals heterostructures transport, optical spectroscopy, and quantum sensing experiments data analysis, modeling, and scientific communication
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, physiology, genomics, transcriptomics, and computational modeling, we aim to (i) determine whether a newly discovered assemblage of diatoms of the genus Diploneis from Lake Tanganyika has diversified through