316 machine-learning "https:" "https:" "https:" "https:" "https:" "U.S" positions at Oak Ridge National Laboratory in United States
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. Successful candidates must be qualified for such access without an export control license. About ORNL: As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year
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preferred. Special Requirements: This position requires the ability to obtain and maintain an HSPD-12 PIV badge. About ORNL: As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL
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the development, implementation, and interpretation of optical plasma diagnostics and integration of real-time data acquisition systems. Experience with machine learning and data-driven approaches to diagnostic
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considered illegal under federal law, regardless of state laws. For foreign national candidates: If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and
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demonstrating procurement knowledge in solving a wide range of complex problems. Learning to lead and manage complex projects for Laboratory customers. Working knowledge of FAR, DEAR and/or DOE procurement
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Qualifications: A BS degree in computer science, computer engineering, information technology, information systems, science, engineering, business, or a related discipline and a minimum of eight (8) to twelve (12
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strengths in any of these areas — quantitative imaging, modeling/transport science, machine learning, or scientific programming — are encouraged to apply. Major Duties/Responsibilities: Lead energy‑storage
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injection techniques, space charge simulation and theory, and control and mitigation of beam halo and other beam loss mechanisms, and machine learning efforts. Provide leadership to the group to support safe
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, regardless of state laws. For foreign national candidates: If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a
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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and