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candidates are expected to have familiarity with cosmology, Bayesian statistical analysis, and strong software skills. CMB data analysis experience is preferred. The Johns Hopkins cosmology group offers a rich
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We are looking for a postdoctoral researcher to develop and implement tools for analysis of output from Bayesian inference under phylogenetic models About the position A postdoctoral researcher
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areas Biomedical applications, social determinants of health or other demographic health areas Spatial microsimulation, spatially weighted regression, combinatorial optimization or Bayesian network
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 2 months ago
the structure from such data is challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine
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Correct), Bayesian statistics, systems theory and artificial intelligence are to be used as a basis in order to explore and implement a continuous calculation chain starting from observable and controllable
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and PhD students. Research spans a wide range. Current interests include: Bayesian statistics; modelling of structure, geometry, and shape; statistical machine learning; computational statistics; high
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closely with data scientists to interpret and predict MFA data using nonlinear reaction-diffusion models, 13C-isotopomer analysis, and MATLAB-based simulations enhanced by Bayesian Machine Learning
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chronobiology on marine animals. Experience with research on deep sea organisms Strong hands-on expertise in big data analyses, non-parametric and bayesian methods for rhythms analyses; panel models, penalized
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, mobility patterns, and reporting delays. The project can include development of software to implement methods, as well as further development of methodology, primarily in a Bayesian framework. There is also
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simulations of reactor core, and other system components Develop reduced-order calibration approaches and apply machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model