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or OpenMP. Experience in heterogeneous programming (i.e., GPU programming) and/or developing, debugging, and profiling massively parallel codes. Experience with using high performance computing (HPC
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, parallel storage systems and scientific data management. Recent research project details and outcomes can be found in computer systems conference proceedings, such as HPCA, FAST, SC, DSN, HPDC, IPDPS, and
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, parallel storage systems and scientific data management. Recent research project details and outcomes can be found in computer systems conference proceedings, such as HPCA, FAST, SC, DSN, HPDC, IPDPS, and
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dynamics (DNS, LES, or RANS) and/or high-performance computing (MPI, GPU, or parallel solvers), as demonstrated by application materials. Evidence of peer-reviewed publications in fluid dynamics, turbulence
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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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computational fluid dynamics (DNS, LES, or RANS) and/or high-performance computing (MPI, GPU, or parallel solvers), as demonstrated by application materials. Evidence of peer-reviewed publications in fluid
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multiphase flow in porous media. 80% - Applying numerical and analytical infiltration models to quantify groundwater recharge potential under varying hydrogeologic conditions. In parallel, the researcher will
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and independently perform experiments in collaboration with computational biologists and other lab members. The Senior Research Assistant will collect, record, and interpret experimental data and
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and/or distributed systems techniques. • Proficiency in programming languages such as Python, C++, or similar, as well as experience with HPC environments and parallel computing. • Demonstrated hands
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well as experience with HPC environments and parallel computing. Demonstrated hands-on experience and understanding of developing scientific data management, workflows and resource management problems. Strong problem