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Field
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density functional theory (DFT) and many-body perturbation theory (MBPT) based methods, in order to predict photophysical properties of novel semiconductors. The theoretical and computational predictions
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density, quantum field theories with strong fields, and neutron-star physics are welcomed to apply. The initial appointment will be for two years, with possible extensions depending on funding availability
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of the group, please visit ORCID: 0000-0002-8591-2652 Research Fields (1) Use density functional theory, many-body perturbation theory, and analytical models to study the novel physical properties of materials
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computational condensed matter physics. We seek a motivated researcher with expertise in density functional theory (DFT) to study emergent phenomena in quantum materials. Research Areas - Correlated electronic
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to apply. We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular dynamics, and materials chemistry. Strong
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systems at various scales, for example using ab initio electronic structure methods like density-functional theory, developing interatomic potentials with various methodologies including machine learning
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the group’s research and philosophy head over to www.d2r2group.com Qualifications Strong background in ab-initio calculations of materials (density functional theory) and high-performance computing
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metal dichaocogenides (TMDs) model the phononic bands of the host material with e.g. density functional theory (DFT) calculate the effect of a strain field on the electronic bands and the phonon modes
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of attoseconds to femtoseconds. The project will involve working with first principles time dependent density function theory (TD-DFT, as implemented in the Elk code) [1-3]. A background including experience in
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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