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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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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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of measurement systems, signal processing and analysis and the assessment of measurement accuracy, robustness and long-term stability. The resulting data form the basis for model-based approaches to evaluating
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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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. Empa is a research institution of the ETH Domain. To strengthen our team and enhance our knowledge and understanding in pyrolysis processes we are looking for a PhD student for scientific analysis
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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
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collaboration funded by the French National Research Agency (ANR) and the Swiss National Science Foundation (SNSF). The project brings together expertise in multifractal analysis, urban and energy planning
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, technologies and systems. The ERAM group within TSL have great experience in SSbD, especially in combining different methods such as modeling mass flows analysis (MFA), Life cycle analysis (LCA) and semi