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: Experience in real-time simulation hardware like Opal-RT and RTDS. Experience with software development. Experience with use of DSPs, FPGAs, advanced computing (e.g., GPUs, QPUs). Excellent written and oral
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Posting Details Posting Details Posting Number S06554P Position Title HPC Research Scientist II Functional Title High Performance Computing Research Scientist II Department High Performance
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to roll out an advisory track in Computer Engineering, we are seeking qualified experts in Computer Networks, Computer Organization, Data Analytics, Cyber Security and Privacy, Edge/Fog/GPU Computing
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to push the boundaries of what’s possible. We work with petabytes of data, a computing cluster with hundreds of thousands of cores, and a growing GPU cluster containing thousands of high-end GPUs. We don’t
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. This position is Exempt from the Fair Labor Standards Act (FLSA) overtime provisions. Benefits at a Glance Want to know your total compensation? Use our calculator to get the complete picture! CU advantage
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
: (1) coordinates all information technology services at the College level; (2) establishes and implements College level computing policy and procedures; (3) represents and advocates
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, Assistant Professor position in the construction area, commencing Fall 2026. The successful candidate will be expected to develop a vigorous and sustainable funded research program in their area of expertise
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of the Empire AI Consortium, researchers have access to state-of-the-art computational infrastructure, including large-scale GPU clusters and high-performance computing resources. The Institute has
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and independent research program, teach effectively at both the undergraduate and graduate levels, and provide service to the department, the university, and the profession. The Aerospace Engineering
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of unparalleled computing resources in the academic environment by optimizing AI/ML models including scaling models across a large set of GPUs; building or optimizing LLMs to tackle new, complex tasks; developing