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Field
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, spanning from the investigation of surface plasmons and the design of nano-structured catalyst materials to density functional theory calculations to understand surface reactions and materials on an atomic
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catalyst materials to density functional theory calculations to understand surface reactions and materials on an atomic level. It provides an international and family-friendly environment that highly
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organic radicals as novel charge transfer systems for use in fuel cells, electrolysers and batteries. Knowledge of computational modelling of materials/molecules using density functional theory (DFT) based
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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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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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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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interdisciplinarity: it combines both, experimental and theoretical expertise, spanning from the investigation of surface plasmons and the design of nano-structured catalyst materials to density functional theory
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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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from the investigation of surface plasmons and the design of nano-structured catalyst materials to density functional theory calculations to understand reaction kinetics. It provides an international and
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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