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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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metagenomics, classical microbiology, and biochemistry, we study their impact on biogeochemical cycles and identify the molecular mechanisms driving these processes. Our long-term goal is to understand
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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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translational research models—SHIELD supports research on therapeutic strategies, novel antimicrobial materials, and experimental models that bridge laboratory discoveries to clinical applications. The program
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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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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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-handed one, known as enantiomers. Their handedness greatly influences molecular interactions, sometimes making the difference between a medical drug and a toxic substance. Therefore, methods for detecting
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. Together with our team of experienced scientists, postdocs and PhD students, you will develop materials that contribute to the development of the next generation of bio-based hybrid materials. The goal
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. Together with our team of experienced scientists, postdocs and PhD students, you will develop materials that contribute to the development of the next generation of advanced hydrogels for wound care