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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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with multitarget estimation for direction-of-arrival (DOA) detection and tracking in radar theory [12]. Graphs are a powerful data structure to represent relational data and are widely used to describe
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Optimization: Mathematical Phylogenetics During this project, you will work on fundamental graph-theoretic and algorithmic problems in mathematical phylogenetics. Job description The Discrete Mathematics and
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across different spatial and temporal scales, from building-level energy demand to district-scale interactions and their integration with wider energy networks. PhD Position in Hierarchical Graph Neural
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models that represent and reason about complex biological systems, enabling predictions and interventions that can alter system behaviour in desired ways. For example, why do cells respond differently