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Directorate. In this role, you will lead the development and application of physics-informed data science and machine learning approaches to support nuclear nonproliferation missions. The successful candidate
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analysis, as well as propose and collaboratively develop new avenues of application for these techniques. Other areas of focus include applications of machine learning and artificial intelligence tools
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science, decision science, discrete algorithms, multiscale methods, experimental computing systems, scalable algorithms and systems, artificial intelligence and machine learning, data management, workflow systems
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modeling, multiscale approaches) to support materials development and manufacturing process understanding. Use AI, machine learning, and data-driven methods as enabling tools to accelerate experimentation
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. Experience with machine learning and data-driven approaches to diagnostic signal processing and real-time control. About ORNL: As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL
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. Experience with machine learning and data-driven approaches to diagnostic signal processing and real-time control. About ORNL: As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL
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expected to contribute to the development and application of advanced manufacturing simulations, and machine learning (ML) models relevant to additive manufacturing, virtual manufacturing, material
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or PhD in Computer Science, Computer Engineering, Cybersecurity, or related fields with 2-4 years of experience. Proven experience architecting and implementing complex distributed systems tailored
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analytics that enhance and evolve business operations and scientific decision-making capability and related activities at ORNL.Qualified applicants will have a solid foundation of Generative AI and Machine
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, JAX etc.) Two or more years of experience in applying machine learning methods for instrument control, such as on a microscope, or on a nanomaterials synthesis platform resulting in publishable