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searching for a computational postdoctoral research associate. The project is associated with atomistic plasma-surface interaction simulations, employing machine learned interatomic potentials (MLIPs
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to investigate and develop novel ceramic electrolytes for next-generation composite solid-state batteries. Particularly halides and sulfides are of interest. By integrating atomistic simulations with experimental
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, or foundation models. Familiarity with atomistic simulations (e.g., density functional theory, molecular dynamics). Interest in developing broadly applicable machine-learning methods for physical sciences
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well as with theoretical researchers specializing in atomistic simulation, density functional theory (DFT), and ab initio molecular dynamics (AIMD). The successful candidate will also engage with collaborators
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to develop novel AI and machine-learning methods for accelerated materials discovery. The focus is on combining generative AI, active learning, first-principles simulations, and machine-learning potentials
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field. Strong computational chemistry background in atomistic simulations, electronic-structure theory, DFT, structure-property relationships, and interpretation of simulation results. Hands-on experience
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for candidates with interests in multiscale simulations of complex physical phenomena, from the atomistic/electronic scale to mesocopics and beyond. Of particular interest is the development and application
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
++, or similar. Preferred Qualifications, Competencies, and Experience Preferred qualifications include experience with molecular dynamics or atomistic simulations, supercomputing or HPC environments, scientific
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
and 2D memristive materials). As a Postdoctoral Research Associate, you will contribute to research in these areas, bridging state-of-the-art atomistic and mesoscopic simulation methods as indicated
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molecular dynamics simulations, applicable to materials science, biomolecules, or a related field. Programming experience (e.g., Python), with a strong background in developing and applying computational