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dynamics (MD) simulations capable of probing hydrogen diffusion and trapping at interfaces in the presence of tramp elements with near-DFT accuracy Collaborate closely with a broad team of researchers from
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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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(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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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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methods such as molecular dynamics (MD) or density functional theory (DFT). • Experience with materials simulation tools or software is preferred. • Knowledge of battery materials, including
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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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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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matter physics, with a staff of 450, including 175 researchers and lecturers. The MEM laboratory (CEA Grenoble) conducts research on the exploration of materials and devices using advanced simulation
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to investigate and develop novel ceramic electrolytes for next-generation composite solid-state batteries. Particularly halides and sulfides are of interest. By integrating atomistic simulations with experimental