178 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" positions at Oak Ridge National Laboratory
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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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in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte
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Excellent written and verbal communication skills and an ability and desire for ongoing learning and growth Aptitude for solving problems and developing and implementing solutions Firm grasp of standard
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of emerging architectures and performance for advanced computing. This position resides in the Architectures and Performance Group in the Advanced Computer Systems Research (ACSR) / Comuter Science Mathematics
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product definition data using computer-aided design (historically Creo). Identify and resolve model, design, and interface problems together with system lead engineers. Provide insights for designing
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, and compliance requirements. Strong aptitude for computer systems, electronic tools, and digital workflows. Ability to learn and adapt to new technologies, including AI-enabled tools used to support
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, assessing hazards for every task, and committing to continuous learning. Other tasks as assigned by management. Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values
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, or office support experience (or equivalent combination of education and experience). Proficient in office computer systems and software applications such as Microsoft Office/Outlook. Must work well in a team
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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