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develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively parallel solver, capable of running
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High-Energy Physics (HEP). We seek highly qualified candidates with interest and experience in ML algorithms including unsupervised techniques, time-series modeling, and clustering algorithms
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technologies, control algorithms and powertrain architectures with focus on advanced technologies. The candidate will assist on projects to benchmark next generation vehicle systems, identify opportunities
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