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applicants with a background or experience in: • Computational chemistry • Materials simulation • Scientific machine learning / AI • Molecular dynamics or DFT • Materials science or a related discipline
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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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techniques into hydrogenase research. To exploit these nonlinear IR techniques to their full potential, computational strategies for simulating 2D-IR spectra of [NiFe] hydrogenases should be established and
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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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(DFT) simulations and develop machine learning potentials to investigate zeolite-related systems. The role will focus on delivering research projects and promoting research excellence in this area. The
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molecular dynamics simulations to model structural, thermodynamic, and electronic properties. - Contribute to open-source software, benchmarks, and datasets that advance the global materials community
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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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-throughput first-principles (DFT/MD) simulations, and generative AI to predict, interpret, and design materials for energy storage, energy conversion, and electronic applications. The successful candidate will
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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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Theory (DFT) Microstructure prediction and evolution Secondary expertise in the following topics is desired but not required: Molecular dynamics Materials informatics Machine learning and artificial