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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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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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-VIS-NIR). Experience in hyperspectral data processing (HMSPL, μXRF, μXAS) and statistical analysis (clustering, machine learning) is preferred. Familiarity with fossilization processes and taphonomic
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, R, MATLAB) for data processing and analysis. - Experience in statistical processing of spatial and temporal data. 4) Instrumentation and metrology: - Knowledge of sensors and measurement instruments
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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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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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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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of statistics is preferred. Experience with experimental work and molecular ecology methods is an asset. The candidate must demonstrate proven ability in independent scientific research and skills in writing