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Overview Molecular spins combine the coherence, optical readout, and room-temperature operation of solid-state spin qubits with the atomistic tunability and nanoscale modularity of synthetic
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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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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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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
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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, 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