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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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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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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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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
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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
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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