84 machine-learning-"https:" "https:" "https:" "https:" "https:" "University of St" scholarships at CNRS in France
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produce high-quality scientific articles of which they are the first author, to disseminate their results. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR6226-JAMBEH-003/Default.aspx
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weekly meetings; +Participating in national and international conferences; +Writing quarterly reports. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR5253-NICBRU-007/Default.aspx
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spectroscopy, optical ranging, and neuromorphic computing, yet their intrinsically multidimensional nonlinear dynamics introduce a level of complexity that remains unresolved. Where to apply Website https
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PRIME (Projet de recherche inter-instituts multi-équipes) interdisciplinary CNRS programme (http://www.cnrs.fr/mi ). The PhD candidate will be co-supervised by Florence Niedergang (Team « Biology of
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. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR6226-CYRPOR-005/Default.aspx Requirements Research FieldChemistryEducation LevelMaster Degree or equivalent Research FieldEnvironmental
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, C.; Pineau, N.; Perrier, A. J. Chem. Theory Comput. 2020, 16 (11), 7017–7032 https://doi.org/10.1021/acs.jctc.0c00762 . (b) Villegas, O.; Serrano Martínez, M.; Le Bras, L.; Ottochian, A.; Pineau, N
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congested architectures. For more information, visit my professional website: https://iscr.univ-rennes.fr/daniel-muller Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR6226-DANMUL-005
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part of the research theme 'Planets and Moons', and will be integrated within the ERC - IceFloods (https://lpg-umr6112.fr/en/erc-icefloods/ ). This thesis will aim to characterize the contribution of ice
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PhD position: Nanoengineering refractory compositionally complex alloys for extreme conditions (M/F)
for innovative bulk materials adapted to the extreme conditions of future energy systems. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UPR3407-MATGHI-007/Default.aspx Requirements Research
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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