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reactions, catalyst surfaces and interfaces, reaction mechanisms, activity and selectivity develop reproducible atomistic simulation and high-throughput workflows using Python, ASE and relevant DFT software
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MLIPs and DFT workflows (e.g., VASP, atomate2). Experience running and scaling simulations on HPC. Broad knowledge of solid-state materials science. Ability to work independently within a large multi
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functional theory (DFT) calculations of NMR parameters, powder X-ray diffraction, and other complementary analytical techniques. The postdoctoral researcher will be responsible for planning and conducting
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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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predictive MD simulations capable of resolving atomic-scale LCI mechanisms with near-DFT accuracy Investigate how silicon suppresses LCI, including its effects on grain boundary site competition and the
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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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generation, targeted atomistic simulations, structural descriptor extraction, and predictive models. The work will aim to establish links between local pore geometry, structural disorder, sodium adsorption
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Computational Nanoscience group at ICN2 develops theory, models and large-scale simulation tools for quantum transport, spin dynamics and emergent computing in low-dimensional materials, in close contact with