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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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spectral graph theory. The PhD will be supervised by Anurag Bishnoi.You will have the opportunity to collaborate with Postdocs, PhD candidates, and other faculty members of the research group. You will also
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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