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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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—particularly in topology, geometry, or related fields—and would also take genuine interest in thinking about how formal models can inform sociological theories regarding the structure and process of science. The
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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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Zerbib. This includes graph theory, discrete geometry, topological combinatorics, extremal combinatorics, and flag algebras. The position has a 2-1 teaching load and a requirement to be involved with
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foundations, focusing on the structure and complexity of quantum computational models; (ii) optimization and related graph-theoretic methods, using polyhedral geometry and algorithmic optimization (e.g., vertex
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Physics, Spectral Theory, Quantum Chaos, Large Graphs and Quantum Walks. Related areas such as Quantum Information can also be considered. This position is offered through the research funds of Mostafa
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chemistry, conventional and enhanced sampling molecular dynamics simulations, generative AI, graph theory and more to characterize biomolecular systems. The project is predominantly an applied project
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expertise: A PhD in Computer Science or a closely related field, with specialization in Quantum computing and Graph theory In this role, you will be responsible for conducting research on graph theoretic
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inequalities and Sobolev-type spaces (with Hytönen and/or Korte), 3. Conformal deformations of metric measure spaces and/or general regularity and convergence for graph-based machine learning using stochastic
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architecture and design for complex socio-technical systems Graph theory, network science, and knowledge representation Agent-based and simulation modeling AI/ML, foundation models, causal inference, and