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include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced
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computational physics, computational materials, and machine learning and artificial intelligence, using the DOE’s leadership class computing facilities. This position will utilize methods such as finite elements
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learned sent to designer staff. Works with US ITER document Control Center to ensure accuracy and completeness of drawings and other engineering design documents in the DCC system Provides other functions
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, and parallel computing, with a proven ability to work within highly secure and regulated environments. This role involves close collaboration with security teams, scientists, and IT leadership to ensure
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environment. Collaborative team orientation and willingness to learn. Basic Qualifications: BS/BA degree in Human Resources, Business Administration, or a related field. Attainment by May of 2026 and applicants
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informed of class logistics. Use the Learning Management System (LMS) for monitoring registrations, determining class assignments, setting delivery schedules, printing rosters, and verifying training records
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SME support and field testing events Document results in technical reports and/or peer-reviewed publications Present findings at academic conferences and to research sponsors Work in a collaborative
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. Work with stakeholders to implement corrective actions, best practices, and lessons learned into existing processes. Flow down requirements from codes, standards, Federal Regulations, and contract
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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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expected to contribute to the development and application of advanced manufacturing simulations, and machine learning (ML) models relevant to additive manufacturing, virtual manufacturing, material