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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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/statistical physics, applied mathematics, or a related quantitative field. A background in the theory of high-dimensional systems and hands-on expertise in the technical toolkit of disordered systems (replica
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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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with studying linear and differential cryptanalysis. Then, a particular attention will be taken at differential-linear attacks or more generally at statistical or composite attacks. We will, after having
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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