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investigate fault slip, stress evolution, and coupled mechanical, hydrologic, and thermal processes in geologic materials. Working with a multidisciplinary team, you will use high-performance computing and
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, high-performance computing, distributed computing, or large-scale workflow management. Experience developing reconstruction, simulation, or detector calibration software. Familiarity with modern machine
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calibration, validation, prediction, or decision support. Experience with high-performance computing and/or modern machine-learning frameworks, such as PyTorch or JAX. Familiarity with AI agents, tool-enabled
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with high-performance computing, GPU programming, parallel programming, cloud computing, and/or related methods including running numerical simulations of complex workflows. Demonstrated technical
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, Crystal Plasticity) Ability to independently develop massively parallelized codes using C/C++/Python and proficiency in high-performance computing environments. Strong analytical and problem-solving
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Description We have an opening for a High Energy Density Science (HEDS) Center Postdoctoral Fellow . The purpose of this fellowship is to attract top HEDS talent to Lawrence Livermore National Laboratory (LLNL
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property predictions (ranging from analytical to machine learning surrogate models) in LLNL’s Materials Acceleration Platform that is deployed on LLNL’s High Performance Computing infrastructure. Develop
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competencies at LLNL. Fellows participate in experimental, computational, and/or theoretical work, and will have access to LLNL's extensive computing facilities, talent pool, specialized laboratory facilities