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Field
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minimum of ten years of experience in high-performance computing, including five or more in a leadership capacity managing large-scale GPU-based systems; a strong technical background in HPC infrastructure
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 10 hours ago
operational improvements. PREFERRED: Familiarity with IT service management frameworks, vendor coordination, and GPU or cloud-based infrastructure. Revised 12/19/2025
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 10 hours ago
GPU computing; scripting in Python or Bash; and container orchestration tools like Docker and Kubernetes; and experience in cloud-based HPC or AI/ML workloads. 12/3/2025
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commonly used on Unix systems. Additional languages or experience with libraries for utilizing GPU hardware efficiently, e.g., CUDA, are a plus. Experience in AI programming with, e.g., PyTorch(-DDP
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GPU utilization and allocate computing resources efficiently across users. c) create and manage user accounts for faculty and students; troubleshoot user issues; and design and deliver
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administrative use of Macs and Windows, as well. The Division's research and teaching bring a strong requirement for GPU compute and mass data storage, with GPU workstations across large parts of the Division, HPC
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foundations. Candidates should possess an exceptional academic record and a strong mathematical background. Experience conducting large-scale computational experiments (e.g., multi-GPU systems) is advantageous
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, shop equipment, and hand tools. Perform routine maintenance on fixed and rotary wing aircraft. Operate and maintain ground support equipment (e.g., tugs, forklifts, GPUs, snow plows, vehicles). Maintain
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good understanding of the physics of scattering and antenna radiation Programming experience in C/C++ is necessary while experience in parallel and GPU computing is most desired More Information Location
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decays and related physics object performance studies , development of the real-time analysis (RTA) in particular with ML/AI reconstruction on hybrid GPU/FPGA architecture for the electromagnetic