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Field
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engineering, chemistry, physics, or a closely related field are particularly encouraged to apply. We seek candidates with expertise in some or all the following areas: density functional theory, deep learning
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combine density functional theory (DFT), molecular simulations, and machine-learning force field (ML-FF) development to uncover the factors controlling NHC–surface interactions and to model realistic
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on electronic structure and phonon dynamics in the part of the phase diagram of TMDs where short-range CDW fluctuations dominate. The methods that will be used include density functional theory and many-body
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will focus on the development of GPU-accelerated GPAW software based on density functional theory (DFT) for constant-potential calculations within a plane-wave framework. The developed software will be
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, 7503–7507 (2015) • collaborate with theoreticians at CEMES to compare experiments with the band structure first-principles calculations based on the density functional theory (DFT). The National Intense
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including strongly correlated fermion materials, high-temperature superconductivity, topological electronic states of matter, developments and applications of computational methods at the density-functional
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, molecular, optical and chemical physics, high energy density and plasma physics, condensed matter physics, astrophysics and atmospheric sciences. Please see here for more information: https://www.unr.edu
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Skills Familiarity with density functional theory and beyond-mean-field techniques. Knowledge of high-performance computing methods. The duties, qualification requirements, and language skills of a
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strongly recommended. • High Performance Computing. • Experience with High Throughput Calculations will be valued but it is not essential. • Previous knowledge of Density Functional Theory (DFT) and
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performing atomistic simulations with Density Functional Theory and Molecular Dynamics. Data analysis and coarse graining in order to provide parametrisations for upper scale models (Kinetic Monte Carlo and