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energy system models that incorporate a stronger Social Sciences and Humanities (SSH) perspective. By embedding societal dynamics, such models aim to capture a wider range of future uncertainties and
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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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. 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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remains limited, with numerous fabrication challenges yet to be addressed. This collaborative project in close collaborration with partners at ETHZ and TU Delft aims to bridge the gap in current medical
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layers, thorough FLA processing, and extensive materials characteri-zation using XRD, electron microscopies, TOF-SIMS, electrochemical methods, etc. Modeling and simulations should help us to explain
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their subsequent simultaneous analysis. This project aims at overcoming these challenges to reliably measure atmospheric levels of PFASs and model their respective emission strengths in Switzerland
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nanoparticle systems. Investigate model particles such as liposomes, mesoporous silica and silver nanoparticles. Investigate RNA-LNP formulations for next-generation gene therapeutics, examining how lipid
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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