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data analysis, simulation, and machine learning, integrating resources across multiple facilities. NERSC's next major supercomputer, Doudna, will combine next generation GPUs, networking and storage
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GPU acceleration (CUDA) Participation in relevant competitions (e.g., Kaggle, computer vision challenges) Experience with version control (Git) and collaborative development practices
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facilities. Core responsibilities include the deployment and maintenance of small-scale HPC and compute nodes, GPU workstations, Linux and Windows servers, research data storage and backup solutions
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well as experience working with large biological datasets in scalable GPU-based computing environments. What we provide: A competitive compensation package, with comprehensive health and welfare benefits. A supportive
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Implement sustainable and reproducible and FAIR research software engineering practices Collaborate with other HPC facilities and project partners Help evaluate and integrate GPU acceleration and other modern
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2026, UTC will deploy a state-of-the-art scientific computing and storage infrastructure, comprising a high-memory, GPU-enabled HPC computing node and a modular, scalable RAID-based storage system
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to reinforcement learning, imitation learning, or learning-based control in simulation. Experience working with mobile manipulators, humanoid robots, or legged robots. Familiarity with GPU-accelerated simulation
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Engine, Unity, Blender, Adobe Creative Cloud, or DaVinci Resolve, with simple version-control tools like GitHub or Perforce. Experience with powerful PCs with strong GPUs, a mix of VR headsets like Meta
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managing all underlying compute, networking, and storage resources (e.g., Kubernetes clusters, GPU instances). Feature Engineering: Define shared data and feature management patterns to ensure consistency
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/CloudFormation) for provisioning and managing all underlying compute, networking, and storage resources (e.g., Kubernetes clusters, GPU instances). Feature Engineering: Define shared data and feature management