46 structures-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" scholarships at CNRS in France
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material for an intended structural application. To conduct this study, a nickel-based superalloy (Inconel 718) will be used as a model material because of its broad industrial applicability and its
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ultrasound, Laboratoire d'imagerie Biomedical, LIB , https://www.lib.upmc.fr/ ) and nanoparticle engineering ( PHENIX Laboratory https://phenix.cnrs.fr/ ). The LIB is located in the Centre de Recherche des
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on the flow in soap films and will help us better understand how surface viscosity affects foam drainage, bubble coalescence, and the aging of fluid interfaces. Where to apply Website https://emploi.cnrs.fr
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of both the filament and the substrate, pressure, gas phase precursor composition) with the film chemical structure and the resulting optical properties will be carried out. The DC candidate will be trained
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to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR5798-NICBAC-009/Default.aspx Requirements Research FieldPhysicsEducation LevelMaster Degree or equivalent LanguagesFRENCHLevelBasic Research
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of internal reports, articles, patents, and communications. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR5635-DAMVOI-021/Candidater.aspx Requirements Research FieldChemistryEducation LevelPhD
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at the Center for Theoretical Physics in Marseille, France. Wikipedia is the classical example of a successful, large-scale collaborative project. Its production, structure and functioning have emerged through
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together expertise in physics, chemistry, nanoscience, and materials engineering. For more information about IS2M, please feel free to visit the website: https://www.is2m.uha.fr/ . The PhD candidate (M/F
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program, involving close collaborations with several French laboratories and NASA's Jet Propulsion Laboratory. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR7326-ANAMEK-122/Candidater.aspx
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Starrydata2). The work will include the implementation of machine learning models (neural networks, random forests, SISSO), generative approaches for predicting crystal structures, the use of machine learning