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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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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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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