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generation of atomic structures and training of machine learning interatomic potentials (MLIPs) in a way that maximizes the capability of high-performance computing (HPC) platforms available in the Oak
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and conferences. To conduct this work, the successful candidate will use the world's first exascale system, Frontier, and collaborate with leading experts in machine learning, optimization, electric
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calibration approaches and apply machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model development. Complete simulation verification, model validation
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Carlo, machine learning or a related field Proficiency in quantum mechanics, statistical modeling, and linear algebra Preferred Qualifications: Experience with density matrix renormalization group and