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, maintaining a healthy respect for what can go wrong, and where learning never stops. Provide technical support in the application of regulatory requirements, procedures, and standards that are applicable under
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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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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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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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, providing visibility of what successful looks like for roles within the group. Establish an environment where learning never stops, honest mistakes are treated as opportunities to learn, and challenges
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, designing and testing solutions, or similar experience or equivalent combination of education and experience will be considered Demonstrated ability to learn and support multiple business areas Ability
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. Preferred Qualifications Familiarity with techniques for AI-on-AI adversarial evaluation, including reinforcement learning-based adversarial testing setups. Expertise in designing systems that support red
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communication and interpersonal skills. Excellent organizational skills with attention to detail. Demonstrated ability to learn and support multiple business areas. Current or previous ORNL experience. Special
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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