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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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programming, modelling, and data analysis skills. Experience with formulating and solving mathematical optimization problems is an asset. Proficiency in English is required; good comprehension and oral skills
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their subsequent simultaneous analysis. This project aims at overcoming these challenges to reliably measure atmospheric levels of PFASs and model their respective emission strengths in Switzerland
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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