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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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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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for the first four full-time equivalent years of your doctoral studies. You will have the opportunity to work with leading national and international researchers – experts in social network theory, qualitative
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of biomolecules which can only be successfully tackled by employing a variety of different theoretical methods. In this respect, this joint graduate college brings together the expertise in analytical theory from
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, which arise from processes such as hybridization, horizontal gene transfer, and recombination. Creating such networks from DNA sequences requires techniques from graph theory, theoretical computer science