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to automate, accelerate and improve decision making. Analysing graph data requires solving problems at the boundaries of machine learning, graph theory, and algorithmics. Your future tasks: You actively
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About the Role The position is funded through the EPSRC project “Zeros, Algorithms, and Correlation for graph polynomials”. We study various combinatorially defined polynomials such as the
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denotational semantics, abstract machines, as well as string diagrams and graph rewriting. Some knowledge of category theory would be useful but not essential. Being able to formalise the frameworks and
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, lost/cost functions, stochastics, computational complexity, logic, graph theory or other areas closely related to it Proven expertise in techniques for descriptive statistics, vectors & matrices and
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and Sobolev-type spaces (with Hytönen and/or Korte), Conformal deformations of metric measure spaces and/or general regularity and convergence for graph-based machine learning using stochastic game
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on graph theory (Dr Felix Reidl) Extraction of Rules and Specifications from Temporal Data (Dr Vladislav Ryzhikov) Novel diffusion model learning algorithms for synthetic DNA sequence design (Dr Cen Wan
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methods such as graph theory and networked systems, with the latest AI-enable data fusion and digital co-simulation technologies for a more resilient cyber-physical power systems, so that cyber can have a
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and machine learning. Topics of interest in this area include, but are not limited to: natural language processing, large language models, graph learning, prompt engineering, knowledge graphs, knowledge
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are not limited to: natural language processing, large language models, graph learning, prompt engineering, knowledge graphs, knowledge engineering, linked data, web technologies. About the role
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optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative AI, or active learning for materials applications. Integration of theory and experiment: Using computation and