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years prior to the application deadline. Experience with machine learning for scientific applications. Experience with deep learning frameworks such as PyTorch or TensorFlow. Experience with atomistic
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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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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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. Experience with atomistic simulations, density functional theory (DFT), molecular simulations, or machine-learning potentials. Experience with generative AI, active learning, uncertainty quantification
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
inform observable macroscopic properties as part of the activities of the UNC Superfund Research Program (SRP) (https://sph.unc.edu/superfund-pages/srp/). This work involves running, developing, and
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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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, particularly machine-learned interatomic potentials, in the context of chemical research. Knowledge of atomistic and coarse-grained classical force fields. Experience creating and maintaining scientific software
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Posting Summary Logo Posting Number RTF00028PO21 USC Market Title Post Doctoral Fellow Link to USC Market Title https://uscjobs.sc.edu/titles/8219 Business Title Post Doctoral Fellow Campus Columbia