186 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" "Univ" Postdoctoral research jobs at CNRS
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), Ryoji Shinya (Meiji University, Japan). Background: Mignerot et al. 2024 https://doi.org/10.7554/eLife.88253.2 Kanzaki et al. 2021 https://doi.org/10.1038/s41598-021-95863-1 Our team (http://ibv.unice.fr
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located in an innovative environment, at the cutting edge of future technologies, in strategic application sectors. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR5270-SYLGON-070/Default.aspx
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postdoctoral students. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR9001-FABOEH-006/Default.aspx Requirements Research FieldMathematicsEducation LevelPhD or equivalent Research
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will involve measurements at synchrotron radiation facilities and visits to collaborating research groups. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR7265-JULORD-012/Default.aspx
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the Swiss team led by Christophe Ballif (EPFL/CSEM). Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR9006-JEAGUI0-017/Default.aspx Requirements Research FieldEngineeringEducation LevelPhD
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impact measurements, AFM imaging, and AFM-SECM experiments. The SEEAFM project will be developed within the Electrochemistry group of the IMF team at the CEISAM laboratory. Where to apply Website https
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e.g., ultra-cold gases of bosonic or fermionic atoms, machine learning technologies and quantum computing. At the same time, we work in close connection with IJCLab experimentalists, particularly
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the oxidation state and the electric-polarization direction in HfO2. Project included in the activities of the MEM group of CEMES, funded by the labex NanoX. Where to apply Website https
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Post-doctoral position (M/F) for testing drought-based BEF relationships at CEFE Montpellier, France
management scenarios. Reference : Postic et al. 2025 GMD. https://gmd.copernicus.org/articles/18/7603/2025/ Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR5175-XAVMOR-003/Default.aspx Requirements
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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