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varied responsibilities in a dynamic, nationally and internationally oriented research environment Access to state-of-the-art experimental and computational infrastructure Attractive employment conditions
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synergies and develop novel ideas are highly valued. PhD students will have access to state-of-the-art computational infrastructure, benefit from internationally competitive employment conditions, and receive
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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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multifractal analysis, urban and energy planning, geography, and artificial intelligence to develop coherent and resilient approaches for urban energy infrastructures under land-use constraints such as No Net
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employment conditions, and strong support for personal and professional development. The PhD student will be enrolled in the ETH Zürich / University of Zürich doctoral program , depending on academic
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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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more cost-efficient. Together, UESL and IMOS are seeking a motivated and qualified PhD candidate to advance the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By