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models, large language models, and statistical mechanics to conduct research in the following areas: (1) Development of data-driven schemes for the discovery of slow variables. (2) Molecular simulations
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on astrophysical source populations and dark-matter signals, exploiting statistical correlations across datasets to break degeneracies that limit single-probe analyses. The framework will be validated on synthetic
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. - Applied Mathematics and Statistics: Knowledge of statistical methods and algorithms for data analysis, including model fitting, regression, and sensitivity analysis. Expertise - Scientific Collaboration
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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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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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, 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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hold a PhD in the fields of ecology or evolutionary biology, or biostatistics/biomathematics. Required skills: - Data analysis using R and database management - Statistical modelling for ecology
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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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- Good communication of scientific results -Knowledge on evolution, statistics, evolutionary genomics, especially for sex chromosome studies (phylogenomics, orthology, selection, synteny, SNPs, structural
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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