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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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10 Oct 2025 Job Information Organisation/Company Empa Research Field Chemistry » Other Environmental science » Water science Physics » Chemical physics Researcher Profile First Stage Researcher (R1
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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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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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computer science, mathematics, or statistics and a strong interest in empirical analysis and interpretation. Areas of specialization can be, for instance, network analysis, computational geometry, or time
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across EPFL’s international research network. Profile Master’s degree in environmental engineering, microbiology, chemical engineering, or a related discipline Demonstrated research experience
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. Empa is a research institution of the ETH Domain. Empa’s Laboratory of Biomimetic Membranes and Textiles develops novel smart fibers, textiles and membranes for body monitoring, drug delivery and tissue
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Marie Curie Grant Agreement Number 101226997 Is the Job related to staff position within a Research Infrastructure? No Offer Description Background Information Marie Skłodowska-Curie Doctoral Networks
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Marie Curie Grant Agreement Number 101226997 Is the Job related to staff position within a Research Infrastructure? No Offer Description Background Information Marie Skłodowska-Curie Doctoral Networks
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on musculoskeletal biomechanics and mechanobiology across all scales. Project background This project is part of the EU-funded Print4Life , a Marie Sklodowska-Curie (MSCA) doctoral network led by Prof