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candidate will join the “Ecosystem Functioning” research group (ECOFUN - https://ecobio.univ-rennes.fr/ecofun-diversity-interactions-processes ) and will be supervised by Cécile Monard (CNRS Researcher) and
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researcher, and a research engineer. Field experiments will take place in Seville for Cataglyphis velox and in Australia for Myrmecia species. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR5169
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acquisition). Statistical analysis and computational modelling of cross-linguistic data from a developmental project investigating the emergence of an indefinite article in Hindi. Oral narratives have already
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spectrometry techniques available in the laboratory, as well as optimizing analytical methods if necessary; Performing advanced data processing, including statistical and multivariate approaches, and potentially
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models. The project will involve statistical, computational, and theoretical components, including: - derivation of environmental covariates that best reflect resource uptake processes above- and
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and international environment. The successful candidate will join Martin Blackledge's group, Protein Dynamics and Flexibility by NMR. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR5075
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l'ENS, within a research environment specialized in soft matter, statistical physics, and fluid dynamics. The project will rely primarily on large-scale molecular dynamics simulations and theoretical
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of Euclid data. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR8262-ELIBLA-002/Default.aspx Requirements Research FieldAstronomyEducation LevelPhD or equivalent LanguagesFRENCHLevelBasic Research
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the framework of the contract. This doctoral project lies at the intersection of geography, economics, statistical physics, and complexity science. It examines the relationships between residential locations and
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obtained in monkeys on implicit statistical learning within our laboratory. • Mastery and adaptation of bio-inspired Hebbian learning models • Evaluation of the ability of these models to account for data