9 bayesian-inference-"Integreat--Norwegian-Centre-for-Knowledge-driven-Machine-Learning" positions at University of Minnesota
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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
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. • Experience with machine and deep learning modeling approaches and developing Bayesian models. • Multidisciplinary skills to bridge fields such as plant disease ecology, remote sensing data, and geospatial
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inferences from observational datasets ● Familiarity with urban ecology, aquatic plant ecology or watershed biogeochemical processes ● Proficiency with GIS About the Department Ecology, Evolution, and
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on applying, developing and implementing novel statistical and computational methods for integrative data analysis, causal inference, and machine/deep learning with GWAS/sequencing data and other types of omic
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multiple statistical and modelling approaches, including Bayesian approaches. About the Department Ecology, Evolution, and Behavior (EEB) faculty teach undergraduate classes, advise graduate students, and
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methods of data analytics (e.g., statistics, stochastic analysis, Bayesian statistical analysis), physically-based hydrology and water quality models, and the use of machine learning tools for modeling flow
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organizational, quantitative analysis and writing skills are necessary. Candidates with a strong background in molecular virology, next-generation sequencing, Bayesian analysis, phylogenetic analysis, statistical
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in the University of Minnesota. The research will focus on applying, developing and implementing novel statistical methods for causal inference, integrative data analysis or/and machine/deep learning
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regulatory network inference, cell lineage reconstruction, multi-dimensional data integration and etc. * Discuss with the laboratory members in bioinformatics analysis as needed. * Maintaining an active role