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& Responsibilities Perform large-scale electronic structure and atomistic simulations (DFT, AIMD, NEB) to study ion mobility in aluminosilicate and oxide frameworks. Generate datasets linking structure, environment
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Computational Cost by Machine Learning and DFT-Based Data, Journal of Chemical Theory and Computation, 2024, 20 (16), 7287–7299. Funding category: Contrat doctoral PHD Country: France Where to apply Website https
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combines state-of-the-art computational multiscale modelling (using DFT/TDDFT methods, collision theory, molecular dynamics, stochastic dynamics, Monte Carlo and analytical methods) and its thorough
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combines state-of-the-art computational multiscale modelling (using DFT/TDDFT methods, collision theory, molecular dynamics, stochastic dynamics, Monte Carlo and analytical methods) and its thorough
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implement a software engine that automates fault model generation, evaluation, and management. Design and implement advanced test generation methodologies (e.g., test algorithms, Design-for-Test (DfT), Memory
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methods and workflows for chemical problems and experience using simulation software Demonstrated experience with various computational chemistry techniques: DFT-, force-field- and/or molecular-dynamics