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of Bayesian estimation theory, stochastic processes, and statistical inference. Proficiency in scientific programming (Python, MATLAB, C++) and software engineering best practices (Git, testing, documentation
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relevant to modern data science (e.g., Bayesian or frequentist inference, information theory, uncertainty quantification, high-dimensional methods). Programming skills in Python and/or R, with evidence of
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experience in one or more of: large-scale data analysis, time-series photometry, spectroscopy, astrometry, Bayesian/statistical inference, and/or software development for astronomical datasets. Department
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field (e.g., geography, resource management, environmental studies/science, or related disciplines) with strong experience in causal inference research. The ideal candidate will be a highly motivated
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computing (HPC) and parallel processing to enable the analysis of massive datasets. Experience in advanced statistical inference (e.g., Bayesian statistics, spectral methods) for extracting robust patterns
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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