198 machine-learning "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "U.S" Postdoctoral research jobs at CNRS
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support machine learning applications for analyzing electron microscopy images of nanoalloys. Model interactions between nanoalloys and carbon substrates to reflect experimental conditions, incorporating
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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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). - Familiarity with machine learning principles and generative/classification models (PyTorch Lightning, torch, scikit-learn, etc.), as well as data/model analysis methods (PCA, t-SNE, etc.). - Proficiency in
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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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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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Astroparticle domains, covering GW, Gamma & X-rays, neutrinos, CR, radio, optical. Within this project, Gammapy will be improved to enable multi-messenger data analysis workflows. Where to apply Website https
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provides a dynamic environment for theoretical research and interdisciplinary collaborations within this leading mathematical community. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR7598
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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