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, numerical optimization, numerical partial differential equations, and parallel computing. The Researcher will join a project developing parallel high-order meshing algorithms from medical images and parallel
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and tuning. Moderate research project experience training large-scale foundation models, especially pipeline/model parallelism. Track record of creating HPC software for numerical methods. Domain
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, including hybrid simulations coupling machine learning with numerical methods, multiscale discretization, nonlocal closure modeling, structure preservation, multilevel and multifidelity machine learning
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and tuning. Moderate research project experience training large-scale foundation models, especially pipeline/model parallelism. Track record of creating HPC software for numerical methods. Domain
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-scale foundation models, especially pipeline/model parallelism. Track record of creating HPC software for numerical methods. Domain expertise in areas like computational fluid dynamics, material
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learning Demonstrated expertise in software and algorithm development, computational methods, data analysis, modeling, machine learning, high-performance and parallel computing, or scientific simulation
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field or method, including, but not limited to, numerical methods, machine learning, or parallel and distributed computing. Expertise in a parallelization method (e.g., CUDA or ROCm, MPI, OpenMP
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, applied math, computational math, or a related field at the time of the appointment. Experience in computational plasma physics, numerical methods, and HPC. Ability to function and thrive in a collaborative
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experience in intensive and parallel computing (Fortran, C, C++, Python). Applicants with a previous background in at least one of the following techniques, • Quantum Monte Carlo (either DQMC, PQMC, DCA, SSE
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#, FORTRAN, Perl, Python) as well as parallel computing; Ability to work effectively in a team environment with a multidisciplinary group of scientists Ability to conduct research with limited supervision Good