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-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources
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Demonstrated research experience with HPC, AI/ML and/or distributed systems techniques. Proficiency in programming languages such as Python, C++, or similar, as well as experience with HPC environments and
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journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation
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systems, high-speed parallel file systems, and archival solutions critical to advancing scientific discovery and innovation. As part of ORNL’s leadership-class computing ecosystem, you will play a vital
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batch schedulers (e.g., SLURM, PBS, LSF) and parallel file systems (Lustre, GPFS/Spectrum Scale). Experience implementing and managing automation and configuration management frameworks (Ansible, Puppet
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. Demonstrated experience developing and running computational tools for high-performance computing environment, including distributed parallelism for GPUs. Demonstrated experience in common scientific 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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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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Requisition Id 15939 Overview: We are seeking a Natural Resources Professional who will focus on conducting activities assigned to the ORNL Natural Resources Management Team (NRMT) by U. S
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. Scalability of Preprocessing Pipelines: Design and implement automated, parallel preprocessing workflows capable of handling multi-petabyte datasets efficiently while reducing throughput bottlenecks. Data