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; developing, interpreting, and applying the two statistical models; and submitting peer-reviewed publications and presenting findings at scientific conferences and stakeholder workshops. Unit URL https
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Sorbonne Université SIS (Sciences, Ingénierie, Santé) | Paris 15, le de France | France | about 2 months ago
spanning gradients of land use and management practices. Bioporosity will be quantified using X-ray computed tomography and analysed using multivariate and classification approaches to build the typology
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University of California, San Francisco | San Francisco, California | United States | about 1 month ago
agreement. The salary range for this position is $72,000 - $154,600 (Annual Rate). To learn more about the benefits of working at UCSF, including total compensation, please visit: https
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learning, multivariate modeling, or data-driven approaches, as well as interest or experience in the integration of neuroimaging with genetic or transcriptomic data, is considered a strong merit. Prior
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The Computer, Electrical, and Mathematical Sciences and Engineering Division (https://cemse.kaust.edu.sa ) at King Abdullah University of Science and Technology (KAUST) invites applications for a
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forest–lake systems Contribute to trait-based and community-level analyses across aquatic and terrestrial taxa Apply advanced statistical approaches, including multivariate methods, to analyse complex
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urban data, forestry data, temporal and multivariate network modelling, and simulation of infection dynamics in Swedish city environments. The doctoral student will work on the design and implementation
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and other intermediate college-level courses. Ability to teach Multivariable Calculus, Linear Algebra, and/or Physics (in addition to Math) is preferred; candidates will teach courses in their fields
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to multidisciplinary projects on heat, environment, and human health. This position is embedded within Project HEATS (link: https://www.linkedin.com/company/project-heats/about ) and related initiatives which combine
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analytic methods including regression analysis, survival analysis, mixed effects models, multivariable analysis, causal inference methodology (e.g., g-methods), predictive modeling, and interprets results