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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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. Scalability of Preprocessing Pipelines: Design and implement automated, parallel preprocessing workflows capable of handling multi-petabyte datasets efficiently while reducing throughput bottlenecks. Data
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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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partnerships program by identifying and cultivating new industrial users for the OLCF resources. Understand the different allocation programs to apply for time on the OLCF systems and their requirements and be
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systems. Expertise with batch schedulers (SLURM, PBS, LSF) and parallel file systems (Lustre, GPFS/Spectrum Scale). Proven ability to lead technical projects from concept through implementation, balancing
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power electronics resources modeling, explore different intelligence algorithms to enhance ease of usage of simulations, and different applications of EMT simulations. Selection will be based
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work environment. If you are passionate about making a difference in the nuclear field and are eager to contribute to groundbreaking research, we encourage you to apply. ORNL offers competitive pay and
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implementation test capabilities supporting high temperature superconducting magnet development in extreme environments for high field applications of interested to ORNL across different directorates