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on developing machine-learning surrogates for electronic structure and electrostatic potential and using these models to predict structural and electronic evolution under applied bias. Methods may include density
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operations is preferred, working knowledge of machine learning and artificial intelligence methods is highly desirable The successful candidate will demonstrate expertise in accelerator physics, accelerator
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performing experiments to acquire data, using and maintaining research equipment and instruments, compiling, evaluating and reporting test results. Knowledge and experience in chemical thermodynamics, kinetics
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or equivalent in the scientific application of this knowledge and practical laboratory experience. Skill in devising and performing experiments to acquire identified data, using and maintaining research equipment