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department currently manages a SLURM-based cluster comprising CPU, high-memory (HMEM), and GPU nodes, supporting a diverse range of computational research. About the role As the Institute’s computational
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GPUs (e.g., B200, RTX 6000 Blackwell Pro, H200) and CPU architectures that, even if they increase complexity, generate the kinds of problems we love! This role in our Operations Team provides support for
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, workstation, remote desktop, GPU, driver, performance, identity, storage, and network-related issues, escalating where deeper platform or infrastructure support is required. Integrate Linux desktops and
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for operating and developing a research infrastructure comprising CPU- and GPU-based HPC systems, petabyte-scale storage, scientific web services, secure environments for sensitive data, and emerging AI-related
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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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microscopy, X-ray imaging, and light microscopy). It supports researchers across three compute scales: laptop-based prototyping (S-Gym), single-GPU workstations (M-Gym), and multi-node HPC at the National
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project GPUs (B200s and cloud credits) and GPUs from the Department of Computing and the College of Engineering (A100s and H200s) The opportunity to continue your career at a world-leading institution and
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Position Summary Serve as the Lead for the team ensuring smooth operation of the Linux cluster consisting of 300+ GPU/CPU compute nodes including parallel filesystems and high-performance network
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 1 hour 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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programming in C/C++, preferably also Rust, and of POSIX, the Linux/Unix kernel or RTOS. Documented competence in parallel and distributed systems, including GPU programming (e.g. CUDA). Ability to explain