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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 5 hours ago
perturbative matching. Experience with lattice QCD software frameworks (e.g., Chroma, QUDA, Grid) and GPU computing. Experience with statistical analysis of large Monte Carlo datasets, including inverse-problem
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trading strategies. We work with petabytes of data, a computing cluster with hundreds of thousands of cores, and a growing GPU cluster containing tens of thousands of high-end GPUs. Depending on the day, we
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or GPU computing environments. Familiarity with version control, containerization, and reproducible-research tooling. Preferred Competencies Work independently and collaboratively within a
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skills include the following: Applicants must demonstrate knowledge of Linux-based computing, high-performance computing, GPU-enabled environments, distributed or cluster-based systems, and AI
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 4 hours ago
supporting GPU computing environments, including NVIDIA drivers, CUDA, GPU scheduling, monitoring, and performance optimization; familiarity with AMD ROCm is desirable. Experience with container technologies
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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performance GPU computing — whether through thousands of commercial GPUs or a handful of Nvidia NVL72 racks — and specialized signal processing hardware. Automation: Develop and implement automated processes
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on-campus multidisciplinary research (fundamental and translational) centers and laboratories, including: The National Center for Supercomputing Applications housing the most performant GPU-based systems
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-to-end GPU timing; document limitations and extrapolation behavior. Implement, test, document, and maintain open-source Python/JAX research software; collaborate with researchers to connect trained models
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systems and underwater imaging. 4. Linux system administration and scientific computing workflows, including environment management, GPU-accelerated workloads, and remote/headless operation. 5. Familiarity