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statistical methods such as dimensionality reduction and Bayesian modeling. This project offers access to a rich, curated clinical dataset and collaboration with leading neurologists, neurosurgeons, and data
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research with young children Experience with computational methods (e.g., Bayesian modeling, drift diffusion modeling, etc.) Equipment Utilized Physical Demands and Work Environment Overview Statement
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by combining all available data, taking advantage of the varying temporal resolution and different time spans that the records cover. This work will involve Bayesian tools developed by our research
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, Statistics, or related fields No experience required Skills: Strong expertise in the theory and application of birth-death and related stochastic processes Proficiency in both frequentist and Bayesian
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of metabolic and cellular properties Phylogenomic analyses of obtained MAGs, including extraction and evaluation of marker genes, performing ML and Bayesian analyses of (concatenated) marker gene sets using
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candidates with strong expertise in Bayesian methods, uncertainty quantification, and/or machine learning applied to nuclear theory. The group’s research spans a wide range of topics including nuclear
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Statistics or Mette Olufsen or Kevin Flores from Mathematics. Applicants with experience in Bayesian modeling, spatial statistics, mathematical modeling, data integration, uncertainty quantification and/or
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forecasting. Familiarity with ensemble methods, Bayesian approaches, and uncertainty estimation. Experience with large-scale or messy real-world data (structured and/or unstructured). Interest in or experience
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project is to develop scalable and privacy-preserving Bayesian computational algorithms. The position is intended for two to three years, with an initial one-year appointment renewable contingent upon
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at the intersection of systems neuroscience and computational modeling. Our lab is broadly interested in Bayesian inference, perception, multisensory integration, spatial navigation, sensorimotor loops, embodied