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sustainable fluorination reactions. Under the supervision of Dr. Chris Ewels, a CNRS Research Director and expert in DFT modeling of nanocarbon materials, the postdoc will lead Work Package 2 (WP2), which aims
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of materials under pressure, with a view to identifying and formalizing rules and concepts; - Applying the DFT/MLIP “Crystal Structure Prediction CSP” methodology to various issues addressed by the research
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experiments (MS/IR and MS/MS CID) to probe intrinsic energetics, structures and fragmentation pathways Integrating experimental data with DFT calculations to build a unified mechanistic framework Host
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approaches based on density functional theory (DFT) have been introduced in recent years. A new research theme, "organometallic structures (MOFs)," has been introduced more recently, proposing the use
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of new flame-retardant electrolyte for sodium-ion (Na-ion) batteries Where to apply Website https://skk.erecruiter.pl/Offer.aspx?oid=4802762&cfg=f9259847cda849b087ecf2d046… Requirements Research
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physical techniques. Responsibilities and tasks: • In-depth and extensive bibliographic study; • Optimization of the crystal growth process through numerical models; • DFT and DFT+eDMFT studies
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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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numerical calculations (DFT...) to achieve the objectives of the project. Develop adequat numerical methods necessary to reach these goals. Study of the Pt/Co/HfO2 interface: - Calculate the thermodynamical
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 29 days ago
environments, · data manipulation and scripting (Python, Perl, etc.), · programming skills (Fortran, C, C++), · experience with DFT calculations and classical molecular dynamics. We offer: · work in a promising
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, electronic properties, and spectroscopic response. A major component of the project will focus on coupling DFT calculations with machine learning models to accelerate spectral prediction, identify robust