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Science, Computer Science, Applied Mathematics and Statistics, Electrical and Computer Engineering, Biomedical Engineering, or a related field. Experience with a deep learning framework like PyTorch. Strong
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of application development techniques (numerical methods, solution algorithms, programming models, and software) at scale (large processor/node counts). A record of productive and creative research as proven by
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environment consisting of mathematicians, computational and computer scientists, and domain scientists conducting basic and applied research in support of ORNL’s mission. Specific responsibilities include
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collaboration within a multi-disciplinary research environment consisting of mathematicians, computational and computer scientists, and domain scientists conducting basic and applied research in support of ORNL’s
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. in Hydrology, Earth system science, Water resources engineering, Computational sciences, Computer sciences or a related field completed within the last 5 years (or expected soon). Demonstrated
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/ computational science or related field completed within the last five years. Experience in AI/ML. Skills in a programing language are commonly used in AI/ML. Preferred Qualifications: Deep experience in
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science, computer science, computer engineering, electrical engineering, and optical engineering, and frequently collaborates with partners in industry, academia, and other government organizations
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through papers, artifacts, and presentations at top-tier venues. Basic Qualifications: Ph.D. in Computer Science, Computer Engineering, a physical/computational science discipline (e.g., physics, chemistry
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Computer Science, Electrical/Computer Engineering, AI/ML, or a closely related field. Demonstrated experience in AI/ML model development, LLM tuning, generative AI, functional safety and risk analysis. Proficiency
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engineering or equivalent field and 0-2 years of research experience. Background in quantitative analysis, mathematical modeling, data science and simulation techniques with expertise in optimization techniques