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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 12 days ago
will look into multivariate extensions, e.g., predicting joint distributions instead of marginals as done in time-series forecasting with equivariant parametrization [8] or diffusion processes [7
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instruments, determines sampling procedures, plans long-term statistical and qualitative studies, manages project databases, executes multivariate statistical testing, applies qualitative coding frameworks
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coursework in multivariable calculus, matrix algebra, probability, and statistics (required) Previous experience estimating econometric models (strongly preferred) Interest in applying to a quantitative PhD
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these studies will be integrated into a unified hydration classification framework. The candidate will contribute to the development of statistical and analytical approaches, including multivariate modelling and
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typical reading development and children with reading problems or disorders, eye-tracking, rapid frequency tagging, multivariate analysis, time-frequency analysis, source modelling and robust quality
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specific requirements: a) Experience in the application of data analysis and machine learning methods to scientific data (e.g. multivariate analysis, chemometrics, neural networks, classification
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coursework in multivariable calculus, matrix algebra, probability, and statistics (required) Previous experience estimating econometric models (strongly preferred) Interest in applying to a quantitative PhD
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coursework in multivariable calculus, matrix algebra, probability, and statistics (required) Previous experience estimating econometric models (strongly preferred) Interest in applying to a quantitative PhD
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sedimentological (grain size distribution), geochemical (XRF, ICP-OES, C/N), and soil physical (density, pore volume, water conductivity) analyses in the laboratory, multivariate statistical evaluation of the data
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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