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the Peng Research Group . The successful candidate will conduct research at the intersection of scientific AI, atomistic simulation, computational catalysis, and data-driven materials discovery. The position
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interiors. This work will rely on large-scale atomistic simulations paired with machine-learning interatomic potentials. Duties: ● The postdoc will generate density functional theory reference data, use
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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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atomistic behaviour and technical aspects, funded by the Federal Ministry for the Environment, Climate Action, Nature Conservation and Nuclear Safety (BMUKN) (subject to funding approval). The project brings
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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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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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on developing AI-driven methods for catalyst and materials discovery, atomistic simulations, and data-driven understanding of complex energy systems. Key Responsibilities: Research Outputs: Expected to lead 1–2
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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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, transfer learning, uncertainty quantification, multitask learning, or learning from sparse and expensive scientific data. Familiarity with atomistic or molecular-simulation software and interfaces, such as
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Domaine Mathématiques, information scientifique, logiciel Contrat Post-doctorat Intitulé de l'offre Using generative AI to simulate chemically disordered nuclear materials at the atomic level H/F