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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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, please see Degree equivalency Experience Candidates should have experience in the following areas: Experience with Bayesian modelling and inference. Experience characterising machine learning models 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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to statistical computing, Bayesian modeling, causal inference, clinical trials and analysis of complex large-scale data such as omics data, wearable tech, and electronic health record, with specific preference