14 parallel-computing "embl" Postdoctoral positions at Lawrence Livermore National Laboratory
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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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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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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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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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Experience with FORTRAN, C/C++, and parallel computing. Experience in theoretical/computational studies of material microstructure evolution involving microelasticity and/or plasticity effects. Experience with
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Experience with FORTRAN, C/C++, and parallel computing. Experience in theoretical/computational studies of material microstructure evolution involving microelasticity and/or plasticity effects. Experience with
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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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Description The Center for Applied Scientific Computing (CASC) within the Computing Directorate, is seeking a Postdoctoral Research Staff Member with a strong background in computational science, scientific
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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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. 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