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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
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with GPU-accelerated computation and high-dimensional data analysis. Enthusiasm for applying AI innovations to real biological and medical challenges. Required Application Materials: Cover letter
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 18 days ago
of data scientists/clinicians and working with unique datasets from multiple academic medical centers (e.g. UNC, UCSF, Mayo Clinic, Memorial Sloan Kettering, etc). Lab dedicated GPU workstations/servers and
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conferences. Qualifications: PhD in computer science with file systems, GPU architecture experience. Proven ability to articulate research work and findings in peer-reviewed proceedings. Knowledge of systems
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troubleshooting and repair skills. Experience supporting the installation, configuration, and monitoring of Nvidia GPU cards and required drivers and software within Linux. Experience supporting researchers in
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for prototyping. Interest and affinity for high-performance computing are necessary for the position. You should have experience with the roofline model and familiarity with a profiler . Experience with GPUs is a
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on the 1D or analytical model) Hybrid simulation approach (e.g., which combine CFD and 1D simulations) High Performance Computing and/or GPU programming for this domain Machine learning algorithms
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of microfluidic devices. Simulation for microfluidics. (CFD) High Performance Computing and/or GPU programming for this domain. Machine learning algorithms for this domain Clean energy solutions (e.g., microfluidic