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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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(finite element modeling, computational fluid dynamics), and proven experience with COMSOL Multiphysics. Knowledge of heat and mass transport processes in heat-sensitive materials and process optimization
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continuum modeling (finite element modeling, computational fluid dynamics), and proven experience with COMSOL Multiphysics. Knowledge of heat and mass transport processes in heat-sensitive materials and
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element modeling, computational fluid dynamics). Knowledge of heat and mass transport processes in heat-sensitive materials and process optimization. Experience in supply chains and hygrothermal
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understanding of district heating and cooling, renewable energy integration, multi-energy systems, and energy conversion and storage technologies. You have strong skills in programming, modelling, and data