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closely related discipline. You have a strong background and prior experience in atomistic and molecular simulation techniques, specifically density functional theory (DFT) and molecular dynamics (MD
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Domaine Matériaux, physique du solide Contrat Stage Intitulé de l'offre Stage BAC+5 Simulation multi-échelle H/F Sujet de stage Ab initio free-energy landscape of lithiated graphite Durée du contrat
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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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learning for materials discovery or quantitative image analysis, DFT calculations for catalyst design, experiences with MATLAB / Python / AutoCAD / COMSOL • Organic synthesis, polymer chemistry, synthesis
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of spectroscopic measurements. Investigate underlying receptor-analyte interactions with the aid of in silico calculations, including density functional theory (DFT) simulations. Apply machine learning approaches
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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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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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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
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that can be benchmarked through physical experiments or computational simulations. The project is funded by the Research Council of Norway and is a collaboration between NTNU and Norsk Regnesentral