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a closely related field. A strong background in quantum mechanics, solid-state physics, and computational modeling. Previous experience with density functional theory or many-body physics (beneficial
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into hydrogen and nitrogen under practical onboard conditions. Successful candidate will develop and apply computational methods, such as density functional theory based atomistic modelling and machine learning
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into hydrogen and nitrogen under practical onboard conditions. Successful candidate will develop and apply computational methods, such as density functional theory based atomistic modelling and machine learning
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for the new green steels compositions, including impurities and tramp elements. These models should enable density-functional-theory (DFT) accurate large scale atomistic simulations of defects including
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density-functional-theory (DFT) accurate large scale atomistic simulations of defects including dislocations, grain boundaries and precipitates, as well as phase diagrams exploration. A key challenge faced
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pathway. Additionally, finite element theoretical modelling and density functional theory calculations will be used to further increase our understanding of the photo-reduction mechanism. Correlating
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effects in magnets, this project will develop Density Functional Theory methodology for multicomponent lanthanide materials. We will study, for example, how application of pressure causes the f-electrons in
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(density functional theory and ab-initio molecular dynamics simulations) with artificial intelligence techniques to parameterize machine learning force fields and kinetic Monte Carlo methods to model