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, United States of America [map ] Subject Area: Computational Science / Artificial Intelligence/Machine Learning Appl Deadline: (posted 2025/11/19, listed until 2026/01/26) Position Description: Apply Position Description
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approaches for using machine learning to analyze X-ray data, particularly Resonant Inelastic X-ray Scattering (RIXS). The position will collaborate with experts in RIXS experiments (Mark Dean), computational
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skills Preferred Knowledge, Skills, and Abilities: * Knowledge of both the theoretical fundamentals and applications of machine learning. * Experience working in multidisciplinary collaborations. * Strong
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reactivity under realistic conditions. A central aspect of the role is the derivation of interpretable descriptors from electronic structure calculations and the application of machine-learning methods (e.g
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veterans and active military members with careers that leverage the skills and unique experience they gained while serving our country, learn more at BNL | Opportunities for Veterans at Brookhaven National
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automates building and modifying surface structures, submitting DFT calculations, post-processing electronic structure and vacancy energies, and extracting machine-learning descriptors for modeling oxygen
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of Stony Brook University. BSA salutes our veterans and active military members with careers that leverage the skills and unique experience they gained while serving our country, learn more at BNL
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University of New York on behalf of Stony Brook University. BSA salutes our veterans and active military members with careers that leverage the skills and unique experience they gained while serving our
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time associated with family planning, military service, illness or other life-changing events. At Brookhaven National Laboratory we believe that a comprehensive employee benefits program is an important
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-doc and/or in an R&D position, excluding time associated with family planning, military service, illness or other life-changing events. At Brookhaven National Laboratory we believe that a comprehensive