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-scale distributed geospatial workflows that require GPU-based high-performance computing (HPC) across multiple network domains. Major Duties and Responsibilities: RSG is seeking a senior researcher who
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documentation of HPC architectures, configurations, and operational procedures. Guide the architecture of the next-generation of GPUs through an intuitive and comprehensive grasp of how GPU architecture affects
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(e.g., Ansible, PowerShell) and scripting languages. Strong understanding of CPU and GPU environments, network protocols, storage solutions, and cloud technologies. Knowledge of security best practices
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environments Experience with parallel computing environments, HPC in a Linux environment Experience with surrogate modeling Experience with data analytics techniques Familiarity with C++ and GPU programming
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environments Experience with parallel computing environments, HPC in a Linux environment Experience with surrogate modeling Experience with data analytics techniques Familiarity with C++ and GPU programming
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partner with ORNL research organizations to enable research excellence and delivery. We work with other clustered computing and HPC groups to help research programs identify the best solutions
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’ experience post-Ph.D. Expertise in power systems and power electronics. Experience in C/C++, Matlab, and Python programming. Experience in power systems software like PSCAD, PSS/e. Preferred Qualifications
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supporting new research and engineering using ORNL’s Frontier exascale supercomputer for its dense GPU-based HPC resources to train, deploy models and create large-scale production datasets for high-impact
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supercomputer for its dense GPU-based HPC resources to deploy models and create large-scale production datasets for high-impact sponsor missions. The candidate will be expected to handle sponsor requirements and
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involves collaboration with multidisciplinary teams across data-rich science domains (e.g., quantum, energy, security, health) and mission-focused programs. You will contribute to, or lead, research on LLMs