330 machine-learning-"https:" "https:" "https:" "https:" "https:" "U.S" "U.S" positions at Oak Ridge National Laboratory in United States
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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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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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and international relocation assistance is available for certain positions. If invited to interview, be sure to ask your Recruiter (Talent Acquisition Partner) for details. About ORNL: As a U.S
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: Active DOE Q clearance Successful completion of the U.S Naval Nuclear Propulsion Program Experience with DOE O 422.1, Conduct of Operations Experience with DOE safety, security, and regulatory requirements
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Qualifications: Active DOE Q clearance Successful completion of the U.S Naval Nuclear Propulsion Program Experience with DOE O 422.1, Conduct of Operations Experience with DOE safety, security, and regulatory
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limited supervision, operate a variety of machine tools to inspect, calibrate, or produce precision parts and instruments. You will be responsible for applying knowledge of mechanics, mathematics, metal
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to national security. This position resides in the National Security Program Office, Intelligence and Special Programs, National Security Sciences Directorate, at Oak Ridge National Laboratory (ORNL). As a U.S
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toward integration of hydropower with battery storage and other technologies. Computational and analytical skills : Demonstrated ability in selecting and deploying machine learning tools (Random Forest
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funded by the U.S. Department of Energy (DOE) Office of Basic Energy Sciences (BES) in the Materials Sciences and Technology Division (MSTD). The successful candidate will be expected to work effectively