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Peterson). "Statistical field theory applied to complex networks” "Quantum geometrogenesis – Graph theoretic approaches to building spacetime” web page For further details or to discuss alternative project
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, particularly Graph Neural Networks (GNNs), which show great promise. These methods have already demonstrated performance at least comparable to current Track Finding algorithms, with significant room for further
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representation language. The thesis will particularly investigate approaches based on attributed graphs to model infrastructures, their topological relationships, inspection-derived observations, and the levels
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tend to hallucinate facts. By contrast, other AI technologies, such as knowledge graphs and formal reasoning engines, are able to reason reliably, but are less good at handling ambiguity. This PhD
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al., “Deep Transfer Learning for Fault Diagnosis”, IEEE Transactions on Industrial Electronics, 2020. • Zhang C. et al., “Graph Neural Networks for Power Systems”, Electric Power Systems Research, 2023
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framework for underlying structures in high dimension such as underlying covariance structure, conditional dependency graphs etc. With the change in data-generating mechanism, these high-dimensional
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, linear algebra, probability theory, (Bayesian) statistics, optimization and elementary graph theory Familiar with machine learning and deep learning Programming experience (Python or Julia) and their
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generalized framework for extremal structural causal models on arbitrary directed acyclic graphs. Our new models will be able to incorporate non-standard extreme directions, which permits the modeling
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formulation. You will build machine learning models linking molecular structure (SMILES descriptors, fingerprints, graph representations) to reaction rate constants, connect molecular dynamics outputs
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components, systems, and the dependencies between them might be captured in a shared structure, and comparing candidate approaches, such as ontologies, knowledge graphs, graph databases, or multi taxonomy