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. In this project, we aim to develop digital tools combining density functional theory (DFT) and machine learning (ML) to accelerate the in-silico design of solid catalysts for the DA process. - Perform
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and département level) containing a range of socio-economic indicators (indices of location, specialisation, density, structure of the working population, etc.) and to integrate into them the effects
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combine density functional theory (DFT), molecular simulations, and machine-learning force field (ML-FF) development to uncover the factors controlling NHC–surface interactions and to model realistic
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topological carrier populations (carrier density, mobility, effective mass, band bending) through Landau level spectroscopy at very-high magnetic field up to 70 T at the LNCMI Veyrat, L. et al., Nano Lett 15
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performing atomistic simulations with Density Functional Theory and Molecular Dynamics. Data analysis and coarse graining in order to provide parametrisations for upper scale models (Kinetic Monte Carlo and
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system combining piezoelectricity and relatively high mobility electron gas: AlGaN/GaN. This will enable several functionalities, the first one being the ability to modulate the 2DEG electronic density
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with experts in climate modeling, geosciences and in renewable energy development have been set-up to respond to these societal issues. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre