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projects, including: The post-holder will run numerical models that simulate the dispersion of greenhouse gases through the atmosphere. These models will be used, in Bayesian inference frameworks
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international projects, including: The post-holder will run numerical models that simulate the dispersion of greenhouse gases through the atmosphere. These models will be used, in Bayesian inference frameworks
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and familiarity with Bayesian Inference and Markov chain Monte Carlo. Please upload your CV, a cover letter (maximum 2 pages) and names and emails of three contactable referees. The School
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background in developing and integrating probabilistic graphical models, Bayesian networks, causal inference, Markov random fields, hidden Markov models, high-dimensional probability, stochastic modeling, and
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human behavior. Preference will be given to candidates with 1) strong knowledge of Bayesian statistical and computational techniques; (2) prior track record of implementing and applying Bayesian
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computational modeling, geometric morphometrics, multivariate and Bayesian statistics, spatiotemporal and spatial modeling (including GIS), causal inference, machine learning, AI, and statistical software
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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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, kernel machines, decision trees and forests, neural networks, boosting and model aggregation, Bayesian inference and model selection, and variational inference. Practical and theoretical understanding
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and acquired or developmental communication challenges to align with existing research in both departments. Desired areas of statistical expertise include Bayesian statistics, causal inference methods
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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