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and comprehensive grasp of how GPU architecture affects performance for datacenter applications, especially Large Language Models (LLMs) Drive the discovery of opportunities for innovation in GPU
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workflows on GPU-accelerated leadership-class and exascale computing systems. Develop methods for automating large-scale modeling and simulation campaigns, including agentic AI and scientific workflow
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massively parallel computing systems, including GPU-accelerated platforms. Develop and maintain scientific software, primarily in Python , using modern practices for software development, testing
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roles on multidisciplinary teams supporting cutting-edge research and engineering projects, deploying workflows on large-scale computing environments, leveraging ORNL’s Frontier for its dense GPU-based
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leadership-class computing systems and institutional clusters. Write robust Linux bash scripts and job submission scripts for SLURM and PBS environments, including multi-node GPU/CPU workflows, monitoring
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programming with one or more common parallel programming models, like MPI or OpenMP. Experience in heterogeneous programming (i.e., GPU programming) and/or developing, debugging, and profiling massively
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, HPC in a Linux environment Experience with surrogate modeling Experience with data analytics techniques Familiarity with C++ and GPU programming Familiarity with Python programming Experience with
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experience in nuclear, mechanical, or chemical engineering, engineering physics, or related field completed within the last 5 years. Experience with Python, C, and/or C++ applied programming