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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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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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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
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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