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Field
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collaboration with NIST and outside experimentalists. Successful candidates will have experience in density functional theory, machine learning and/or quantum techniques with some exposure to atomistic modeling
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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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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 | 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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collaboration with the Ahn lab, we carried out molecular dynamics (MD) simulations of inactive and active states of ERK2, each extended out to 360 µs of total sampling [3,4]. The findings revealed differential
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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