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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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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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new generation of catalytic nanomaterials for water treatment, made of Earth-abundant elements and designed for their long-term performance. To fabricate and make these materials durable, the core
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engineering applications. Empa's Centre for X-ray Analytics develops X-ray analytical and imaging methods for understanding materials structure in material, life- and medical sciences. This is a joint PhD
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for their long-term performance. To fabricate and make these materials durable, the core concept is the use of plasma technology. PhD position in catalytic plasma coatings for water treatment Your tasks This PhD
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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
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. Empa is a research institution of the ETH Domain. At Empa’s Centre for X-ray Analytics, we investigate bio-nano assemblies from lipid nanoparticles (LNPs) to polymer-based nanosystems using powerful