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develop (physics-informed) hierarchical graph neural network architectures that can capture the complexity of multi-scale urban energy infrastructures. The PhD will explore how these models can represent
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the complexity of multi-scale urban energy infrastructures. The PhD will explore how these models can represent spatial and temporal dependencies in systems, such as building energy demand, district
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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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