16 parallel-processing "Computer Vision Center" Postdoctoral positions at Lawrence Livermore National Laboratory
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Description The Materials Science Division (MSD) at Lawrence Livermore National Laboratory (LLNL) is seeking a highly motivated Postdoctoral Researcher to investigate high-temperature processing and reaction
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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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independent researcher with a high level of attention to details. Proficient with Python and the use of high-performance computer platforms for large parallel simulation and processing tasks. Contribute to
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. Applications area includes but not limited to multimodal SciML models and deep surrogates for various simulations. This position will be in the Machine Intelligence Group in the Center for Applied
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, and materials kinetic processes in materials. Design and perform systematic computer simulations on LLNL supercomputers to establish the foundational understanding of thermodynamic and kinetic
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, and materials kinetic processes in materials. Design and perform systematic computer simulations on LLNL supercomputers to establish the foundational understanding of thermodynamic and kinetic
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design new experiments to further our scientific understanding. Conduct research using massively parallel computer platforms to solve scientific challenges. Publish research results in peer-reviewed
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mechanics. Familiarity with earthquake physics, induced seismicity, geothermal energy, carbon storage, or subsurface engineering applications. Experience with parallel computing, scalable workflows
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diffraction in plastically deformed alloys. Implement the framework in parallel simulation codes intended to run on LLNL supercomputers. Design and conduct complex molecular and dislocation dynamic
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. Develop reconstruction, simulation, and analysis software using modern C++, Python, ROOT, and HPC/Grid computing resources for processing multi-petabyte experimental datasets. Develop and apply state-of