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(e.g., maximum parsimony; Bayesian inference), and utilizing these phylogenies to quantify evolutionary rates, directionality of trends, and patterns of morphological space occupation. You will have (or
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Review, update, and consolidate methodologies, including Bayesian methodologies, in the context of material balance evaluation Your Profile: PhD in applied mathematics, computer science, physics, or in
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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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theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with
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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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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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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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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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behavioral and/or neuroimaging experiments. (2) A strong technical background in Bayesian and reinforcement learning models. Please apply with your CV For people in the EU, click here for information on your
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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