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, 2022) and extended this to the triple equivalence between neural dynamics, Bayesian inference, and algorithmic computation (Commun Phys, 2025). -We validated it within in vitro neural networks (Nature
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on the following activities related to Bayesian inference in factorial designs: Construction and elicitation of informed prior distributions; Critical assessment of default prior distributions; Organizing a many
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. Advance Bayesian and ensemble learning approaches for non-stationary temporal processes. Implement probabilistic diffusion or generative models for long-term forecasting. Collaborate closely with
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standards. About the research project The postdoctoral project will focus on precision tests of low-energy strong interactions via the ab initio modeling of open-shell, nuclear many-body systems and Bayesian
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uncertainty from climate projections into land-use forecasts. Advance Bayesian and ensemble learning approaches for non-stationary temporal processes. Implement probabilistic diffusion or generative models
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, Uncertainty quantification, Approximation Theory, Applied Probability and Bayesian statistics, Optimal Control and Dynamic Programming. Appointment, salary, and benefits. The appointment period is two years
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. Desirable Expertise in computational fluid mechanics, broadly construed. Expertise in Bayesian methodology for optimization and experiment design. Experience with equivariant neural networks. Track record
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career scientist with background in organic geochemistry, statistics, and Bayesian modeling to pursue analyses of paleoclimate biomarker data. The ideal candidate should be proficient with both laboratory
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spatial and temporal scales, leveraging cutting-edge hierarchical Bayesian modeling approaches. The Fredston Lab uses large datasets, theoretical models, and a range of statistical tools to predict marine
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-series photometry, spectroscopy, astrometry, Bayesian/statistical inference, and/or software development for astronomical datasets. Department Contact for Questions Songhu Wang (sw121@iu.edu) Additional