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, graduate, and undergraduate students to advance project goals. Provide technical guidance and mentoring on CFD, numerical methods, and high-performance computing workflows. 15% - Publication & Dissemination
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, and a solid understanding of numerical analysis and familiarity with the use of analytical tools. They should also have knowledge and experience in parallel coding and spectral methods. They must have
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for simulating two-phase flows by integrating advanced Artificial Intelligence (AI) techniques with traditional computational fluid dynamics (CFD) methods. The role focuses on transitioning legacy CFD solvers
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parallel clusters Integrate existing physical models into new software infrastructure for EOS research Benchmark against existing methods and support reproducible, open-science practices Collaborate closely
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for massively parallel computers. Experience with quantum many-body methods. Preferred Qualifications: A strong computational science background. Familiarity with coupled-cluster method. Understanding
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opportunities for parallelism of the completion process, highlighting the potential for significant speedup in computations. Job responsibilities Research and Development: Conduct research to develop novel
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The National Energy Research Scientific Computing Center (NERSC ) at Berkeley Lab seeks a highly motivated Postdoctoral Researcher — Scientific Machine Learning (NESAP) to join the Workflow
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for large samples at ESRF ID16A using multislice tomography approaches. You will lead the development of and work with parallelized computer models to simulate how coherent waves travel through materials with
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image processing and analysis method development. The position builds on the lab's track-record in the field of computational imaging techniques for super-resolution microscopy and image analysis
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tight AI-simulation coupling. What is Required: PhD in Physics, Chemistry, Computational Science, Data Science, Computer Science, Applied Mathematics, or a related numerical field. Programming experience