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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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for every task, and never stop learning. Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal
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environments for several hours at a time; lifting equipment up to 30 lbs) Ability to proactively work independently and as part of a team Demonstrated ability and willingness to acquire new knowledge and learn
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must be able to acquire and maintain appropriate training and qualifications to work in these areas and follow all safety instructions Excellent written and oral communication skills Motivated self
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to get and hold a security clearance A commitment to lifelong learning Preferred Qualifications: 5 or more years of experience relevant to the job duties. Experience with modelling and simulation
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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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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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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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(for example, using reinforcement learning) to inform policies to drive atomic manipulation in microscopy experiments leading to discovery and creation of novel states of matter. In addition to fundamental