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Responsibilities Theory Quantum transport modeling using NEGF; first-principles materials and interface calculations using DFT (VASP, Quantum ESPRESSO, or equivalent). Atomistic spin dynamics
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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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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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will elucidate how silicon disrupts copper wetting and diffusion. A central aspect of this project is the development of a Density Functional Theory (DFT)-accurate machine-learned interatomic potential
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development of a Density Functional Theory (DFT)-accurate machine-learned interatomic potential (MLIP) for the multi-component steel system of interest. Ultimately, this simulation-driven framework will allow
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theoretical research on magnetic and topological properties in van der Waals materials using Density Functional Theory (DFT) calculations, tight-binding and machine learning methods. Provide theoretical
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(Kubo / linear-response and non-equilibrium Green function formalisms) and implement them numerically in the group codes, on tight-binding and DFT-derived (Wannier) Hamiltonians. Validate the extended
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, for DFT activities Where to apply E-mail [email protected] Requirements Research FieldEngineering » Materials engineeringEducation LevelPhD or equivalent Skills/Qualifications The ideal
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/careers.html?id=4306 Requirements Research FieldPhysics » Applied physicsEducation LevelPhD or equivalent Skills/Qualifications A PhD in Physics, Chemistry, Materials Science or related disciplines obtained
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of spintronics. Complemented with density functional theory (DFT) calculations to build scientific and technical competence as well as strengthen transferrable skills, this position provides you with the skills