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Field
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to provide reliable predictions and support effective decision-making. The aim of the PhD project is to address these gaps by developing theoretical and computational tools that combine game theory, graph
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given the increased pressure from human activities that push species to extinction and potentially disrupts ecosystem functionality. Our interdisciplinary lab will develop novel Graph Representation
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people represent environments through concepts such as mental models, cognitive maps, and cognitive graphs. These approaches have provided important insights into how people perceive locations, learn route
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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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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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, 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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, 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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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