200 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "The Institute for Data" PhD positions in France
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• Openness to interdisciplinary research (climate and social sciences) • Good level of scientific English Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UAR636-ALERUB-040/Default.aspx
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, climate, soil properties) and biodiversity data from the citizen science program OAB (Observatoire Agricole de la biodiversité, https://www.observatoire-agricole-biodiversite.fr/ ), the PhD thesis will
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for administrative registration. See: https://www.ehess.fr/fr/doctorat-anthropologie-sociale-et-ethnologie • Applications must include the following documents: - a CV - a cover letter + 2 letters of
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natural organic matter well characterized extracts (IHSS), the study will be performed on real water resources. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR8516-JUSCRI-009
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mathematical models using a reasonable number of physical variables; (2) the observations of extreme weather events used in the learning bases of DNNs are much rarer than those of standard events. This induces a
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | 3 months ago
) the exploration of mixed-precision arithmetic in the context of high-order discontinuous discretization methods, and (2) the integration of machine learning techniques to complement and enhance traditional
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.) as well as the basics of spectroscopy is desirable. Programming skills (Python) and experience or a strong interest in machine learning and data analysis are also expected, given the post-processing
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Redox Monolayer. Phys. Rev. Lett. 130, 218001 (2023). Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UPR8001-SIMGRA-002/Candidater.aspx Requirements Research FieldEngineeringEducation
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, Department of Computer Engineering. Founded in 1981, Heudiasyc has been closely linked to CNRS since its creation and is affiliated with its INS2I institute (Information Sciences). Heudiasyc’s research focuses
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growth methodology based on real-time growth monitoring enabled by advanced in situ characterization tools (RHEED, ellipsometry, curvature measurements, flux monitoring), coupled with machine-learning (ML