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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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, based on measured air temperature and humidity data in cold chains by commercial sensors, and deploy them in end-to-end virtual supply chains. This project also aims to optimize other thermal processes
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some products decay faster. For that purpose, we develop digital twins of the cargo, based on measured air temperature and humidity data in cold chains by commercial sensors, and deploy them in end
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temperature and humidity data in cold chains by commercial sensors, and deploy them in end-to-end virtual supply chains. This project also aims to better understand the tradeoffs related to cooling technology
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project (Decarbonization of Heating and Cooling), we are seeking a motivated and qualified PhD candidate to design integrated district heating and cooling systems. Future thermal networks based on renewable