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considered an advantage: Density Functional Theory (DFT), Molecular dynamics simulations, Machine learning methods for materials modelling, Computational materials science, Organic or hybrid materials
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position within a Research Infrastructure? No Offer Description Activities and context: The fellow will develop machine-learning interatomic potentials (MLPs), trained on density functional theory-DFT data
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. An ideal candidate should have experience in modeling electrochemical reactions on surfaces and interfaces using first-principles density functional theory (DFT), grand canonical DFT (GC-DFT
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carry out analyses using vibrational/electronic static and transient spectroscopy • You conduct DFT calculations # Organic synthesis • You synthesize substrates • Based on our
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will elucidate how silicon disrupts copper wetting and diffusion. A central aspect of this project is the development of a Density Functional Theory (DFT)-accurate machine-learned interatomic potential
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to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR8023-ANTSAI-004/Default.aspx Requirements Research FieldPhysicsEducation LevelPhD or equivalent LanguagesFRENCHLevelBasic Research FieldPhysicsYears
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, for DFT activities Where to apply E-mail [email protected] Requirements Research FieldEngineering » Materials engineeringEducation LevelPhD or equivalent Skills/Qualifications The ideal
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. Experience in solid-state batteries and well-acquainted with electrochemical characterization techniques of batteries. Direct first-hand experience with both inorganic synthesis and DFT modeling including wet
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, please contact Duy Le at [email protected]. Responsibilities: The Postdoctoral Associate’s Responsibilities include but are not limited to: Performing computational modeling using DFT, GC-DFT, MD (AIMD
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Emission Spectroscopy (XES) will be implemented. See http://www.elettra.eu/elettra-beamlines/xafs.html for more information. Job description The successful candidate will join the beamline staff in