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Theory (DFT) calculations using established codes (e.g., VASP, FHI-aims). Demonstrated experience with traditional methods for modeling atomic site disorder, such as special quasi-random structures (SQS
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methodological innovations that bridge the gap between computational theory and impactful clinical application. We are seeking a highly motivated individual with a strong statistical and machine learning
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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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Participate in seminars, workshops, and conferences related to combinatorics and graph theory The successful candidate will hold a PhD in Mathematics or a closely related discipline, with a strong background in
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, group theory, and/or graph theory will be necessary. Experience in modelling biological processes, and in algorithm development or computation will also be valuable. Proven commitment to proactively
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under the direction of senior investigators. Essential Function Yes Percentage of Time 30% Job Duty Apply computational approaches (e.g., connectomics, graph theory, machine learning) to examine brain
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Functional Theory (DFT), machine-learned force fields (MLFF), graph neural networks (GNNs), or large language models (LLMs). Extensive Knowledge In: • First-principles atomistic simulations with packages
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in the following areas: Deep Learning, Scientific Machine Learning, Stochastjc Gradiant Descent Method, and Numerical PDE’s - Advised by Dr. Yanzhao Cao Probabilistic Graph Theory (Network Traversal
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and statistical mechanics. The main research areas include strongly correlated systems such as the Abelian sandpile; random interfaces such as the Gaussian free field; stochastic processes on graphs
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, control theory, data science, data driven methods, discrete mathematics, graph algorithms, high-performance computing, integral equations and nonlocal models, linear and multilinear algebra, machine