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tracking), dataset curation, HPC/GPU programming, blockchain for secure data, C-family languages, and embodied AI/robotics are a plus. Experience with general network resilience, cellular automata
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vision systems (e.g., NVIDIA Jetson Nano) Real-time processing and GPU acceleration Experience working on industry R&D projects Key Competencies Able to build and maintain strong working relationships with
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
. The postdoctoral scholar will be expected to improve on existing GPU-accelerated ocean models and develop laboratory experiments (in the Joint Fluids Lab at UNC), analyze results, publish in peer-reviewed journals
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(Jubail) dedicated to the science division, several GPU-based clusters at NYUAD, and other supercomputer facilities through the CASS network. NYUAD also has guaranteed observing time on the Green Bank
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the College of Engineering. UNLV GPU Cluster (named RebelX) is also available for A.I. research and education. Detailed information about the CEEC Department can be found at: http://www.unlv.edu/ceec MINIMUM
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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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gun, associated diagnostics resources, and well-equipped high-speed and reacting flow laboratories. The National Center for Supercomputing Applications houses the most performant GPU-based systems and
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contact, as identified by AFRL through recent past efforts. This includes the implementation of relevant algorithms and solvers for distributed GPU computing within the JAX Python library. Qualifications
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%) Use tools to identify opportunities to improve performance or understand resource use (utilization of cores, memory, GPU, etc.) in AI workflows. Engage with Ohio, regional and national professional
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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