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areas, including generative modelling (e.g. diffusion models, flow matching, self-supervised and autoregressive approaches), causal machine learning, graph neural networks, dynamical systems modelling
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, storm activity, and other hazards using graph-based clustering, fuzzy machine learning, and reduced-order models – delivering scientific insight into where and when rerouting is needed. Real-Time Decision
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including: * Algorithmic game theory * Approximation algorithms * Automata and formal languages * Combinatorics and graph algorithms * Computational complexity * Logic and games * Online and dynamic
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graph, and discrete random processes. The aim of this project is for the student to develop an understanding of these tools and to apply these techniques to open research problems in the field. Entry
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., symmetries), learning on structured domains (e.g., graphs, manifolds) (to achieve data efficiency and respect constraints) Uncertainty Quantification: building models that quantify uncertainty associated with
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interest in expanding their knowledge in both domains. (1) Geometry/Topology -related methods in computer science. (2) Machine Learning. (For example, graph neural networks, generative networks, or neural