316 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "FORTH" positions at Oak Ridge National Laboratory in United States
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in code review sessions. Demonstrating feature implementations to product owners for acceptance. Basic Requirements Successful candidates will have either a B.S. degree in computer programming or
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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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needs. By leveraging advanced simulations, machine learning, and data-driven insights, the group enables more effective operations aligned with evolving energy demands. The group also develops hydrologic
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) with questions related to this position. Major Duties/Responsibilities: Develop and apply machine learning models (ML) as surrogates for high-resolution process-based hydrologic models. Design and
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economic benefits for the nation. Our partnerships with U.S. Department of Energy (DOE) laboratories, universities, and industry enable us to accelerate science and innovation. The Office of Research
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, substation, corridor scenarios) Integrate physics-informed machine learning models with signal processing feature extraction Develop prototype software tools for automated waveform analytics and real-time
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Ridge National Laboratory (ORNL). Major Duties/Responsibilities: Develop software as a part of a team of research application software engineers, computer scientists, and engineers. Work with researchers
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ability to deal effectively with a variety of individuals at all levels Must have high level of organization and computer skills, working knowledge of PC, Microsoft Office (Word, PowerPoint, Excel, Access
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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