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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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teams from universities, research institutions, and museums in a highly collaborative network, supported by the Muoniverse Research School, which coordinates training, exchanges, and career development
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heritage. Muoniverse brings together 30 research teams from universities, research institutions, and museums in a highly collaborative network, supported by the Muoniverse Research School, which coordinates
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synthesis and characterization infrastructure, including multiple gloveboxes, controlled-atmosphere furnaces, high-energy ball mills, DSC-TG-MS, Raman, FT-IR, XPS, XRD, SEM and more than 1200 battery cycling
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, signal in-tegrity) under varying physiological and environmental conditions Conduct statistical evaluation and model validation using controlled measurements and sensing-dummy reference data Collaborate
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crucial insights. In this project, you will contribute to the development of AI-driven methodologies for experimental fluid mechanics , focusing on: Designing multi-fidelity neural networks for adaptive
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, geography, and artificial intelligence to develop coherent and resilient approaches for urban energy infrastructures under land-use constraints such as No Net Land Take. The consortium comprises four
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offer a full suite of advanced battery materials synthesis and characterization infrastructure, including multiple gloveboxes, controlled-atmosphere furnaces, high-energy ball mills, DSC-TG-MS, Raman, FT
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communication, are required for the project. Our offer Empa offers outstanding infrastructure, career support, networking opportunities, and competitive salaries. Empa also provides excellent infrastructure and
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and flow field interactions Tuning of the CFD models with experimental results Artificial Neural Network training and development Scientific publications in journals and at conferences Supervision