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NIST only participates in the February and August reviews. There is a growing need for high-performance materials for various technological applications. To address this need, the NIST-JARVIS (https
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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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the context of chemical research. Experience with molecular dynamics simulations, applicable to materials science, biomolecules, or a related field. Programming experience (e.g., Python), with a strong
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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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to sustainable energy technologies. The position aims to strengthen and expand DTU Energy’s internationally recognized activities in computational materials science across electronic, atomistic, mesoscopic, and
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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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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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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
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, Chemistry, or a related discipline. Required Certification, Licensure/Other Credentials Preferred Qualifications Knowledge/Skills/Abilities Experience in scientific programming, atomistic simulations and/or