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interiors. This work will rely on large-scale atomistic simulations paired with machine-learning interatomic potentials. Duties: ● The postdoc will generate density functional theory reference data, use
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and machine learning with density functional theory, or other similarly relevant computational methods, to advance understanding of materials design predictions for 2D and 3D systems with electronic and
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computational physics, chemistry, and materials science. Research experience involving density functional theory and ML simulations and is Familiarity with software packages such VASP, CP2K, Gaussian, Scikit
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of Prof. Georg Madsen, with regular shorter research stays at Aarhus University. The project combines density functional theory (DFT), machine-learned force fields and atomistic simulations to uncover how
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economy. As a postdoctoral researcher, you will unravel how silicon suppresses liquid copper infiltration at the atomic scale, using density functional theory-accurate machine-learned potentials and
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Max Planck Institute of Microstructure Physics, Halle (Saale) | Halle, Sachsen Anhalt | Germany | 2 months ago
related field. Experience in one or more of the following areas is desirable: Density functional theory and electronic-structure calculations Wannier-based methods and quantum transport theory, such as Kubo
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National Energy Technology Laboratory (NETL) | Albany, New York | United States | about 19 hours ago
will participate in conducting multiscale modeling such as quantum mechanics density functional theory calculations, molecular dynamics simulations, Monte Carlo simulations, and/or machine learning
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at the atomic scale, using density functional theory-accurate machine-learned potentials and molecular dynamics simulations, in close collaboration with leading European research institutes and steel industry
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position within a Research Infrastructure? No Offer Description Activities and context: The fellow will develop machine-learning interatomic potentials (MLPs), trained on density functional theory-DFT data
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to density functional theory (DFT) calculations and the generation of high-quality atomistic datasets for the development of analytical bond-order potentials (ADP) and machine-learned interatomic potentials