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Field
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training
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for scientific computing to support users in the process of solving increasingly complex compute- and data-intensive problems in science and engineering on high-end parallel and distributed computing platforms
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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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Science. The research focus of our group is on parallel computing, supercomputing, and performance tuning and optimization of advanced applications. Our team currently consists of 10 scientific and 3 administrative
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completion) in applied mathematics, computer science, or a closely related field. Strong background in numerical linear algebra, algorithm design, and parallel computing. Proficiency in programming languages
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volume, OpenFOAM, parallel computing, HPC. Knowledge and experience in suspension fluid mechanics, continuous modelling of granular media, non-Newtonian fluids, and smoothed particle hydrodynamics will be
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and organizational skills. Sound decision making skills. Working knowledge of personal computers. High level of independent thinking. Preferred Qualifications: Extensive customer service experience
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preferably) Strong skills in turbulence modelling, CFD mesh generation and use of parallel computing Have relevant experience in working with aerosols and droplets Proficient in handling large data sets and
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critical responsibilities. Key duties include microscopic FISH analysis, karyotyping analysis utilizing computer systems, FISH wet lab procedures, slide hybridization, PET slide preparation, and proficiency
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; or Experience with high-fidelity solvers, e.g. SU2, OpenFoam, StarCCM+, Fluent; Proficiency in programming, e.g. Python, Matlab, C; Experience utilizing high-performance computing (HPC) to parallelize workflows