64 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" PhD positions at CNRS in France
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of sorption phenomena in these systems. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR5253-PHITRE-003/Default.aspx Requirements Research FieldChemistryEducation LevelMaster Degree
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part of this project, the thesis will focus, on the one hand, on a detailed analysis of gas phase inhibition kinetics by combining experimental and numerical studies to determine global parameters (auto
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) - Signal processing and image analysis (Python) - Oral presentation of scientific results at meetings and international conferences. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR5295
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specialist for the formulation of model soft media. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR5295-MATMAL-001/Default.aspx Requirements Research FieldEngineeringEducation LevelMaster
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new thermoelectric materials using data science and machine learning methods applied to materials, based on expert-reviewed experimental data from the literature and public databases (notably
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The expert will participate in the necessary methodological developments and analyses of airborne data recorded by the IAGOS research infrastructure (https://www.iagos.org ) and from other networks, to provide
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forces on each mode in order to reduce (i.e., cool) their individual vibrations. The student will be closely guided by the advisors and will acquire both theoretical and experimental skills
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on the interaction between human cognition and language—understood as a cognitive entity, a means of communication, an object of learning and lifelong development, and a sociocultural phenomenon. Mission : Background
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Jupiter's polar regions using computer simulations. The core of the project consists of coupling a photochemical model (developed and used in numerous planetary applications) with an electron transport model
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, L. Estel, Analysis of thermal runaway events in French chemical industry, Journal of Loss Prevention in the Process Industries, 62 (2019) 103938. https://doi.org/10.1016/j.jlp.2019.103938 2. Y. Wang