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Field
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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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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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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
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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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following technology areas: hardware/software co-design, performance optimization with heterogeneous and alternative computing systems (CPU/GPU/NPU/etc.), FPGA design, high-performance computing (HPC
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to apply; we value employees with a willingness to learn. Understanding of technologies employed in research in higher education Familiarity with distributed computation solutions Familiarity with GPU
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to apply; we value employees with a willingness to learn. Understanding of technologies employed in research in higher education Familiarity with distributed computation solutions Familiarity with GPU