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of aerosol chemical composition profiles. These methodologies will be extended to monitor stratospheric sulfate aerosols within the framework of the ESA STATISTICS and STATISTICS-II projects. The successful
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, integrating statistical inference, machine learning, and population genetics. We will develop advanced computational methods to characterize the functioning of T- and B-cell repertoires. The goal is to build
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, statistical analysis and visualization of genomic and phenotypic data. * Interpretation of experimental and bioinformatic results in the context of bacterial adaptation to stress. * Contribution
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additional years Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR8079-TATGIR-024/Default.aspx Requirements Research FieldBiological sciencesEducation LevelPhD or equivalent Research
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data) will help validate observations and refine predictive models. Automated monitoring tools (scripts, dashboards, alerts) incorporating machine learning algorithms or statistical methods will be
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(statistics, bioinformatics) to identify correlations between siderophore production, microbiota composition, and colonization resistance. - Present results clearly and rigorously (team meetings, reports
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, GC-MS/MS, and advanced NMR approaches. - **Activity 4:** Multivariate statistics and machine learning to identify microbial and chemical biomarkers of resilience and reveal the interactions linking
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is referred to as cryptanalysis. It is the central focus of this thesis. This PhD project will be dedicated to the study of structural and statistical cryptanalysis techniques, with a stress on
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and water content. Statistical analyses of satellite data describing forest dynamics, and their correlation with pre-defined climatic indicators for the dry season. The Institut des Géosciences de
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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