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Field
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entitled “Beyond Data-Augmentation: Advancing Bayesian Inference for Stochastic Disease Transmission Models”. The overarching aim of the project is to develop the next generation of statistical tools
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computers lack such abilities. The goal of the Adaptive Bayesian Intelligence Team is to bridge such gaps between the learning of living-beings and computers. We are machine learning researchers with
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-country survey datasets for comparative analysis. Conceptualize and refine a theoretical framework integrating intersectionality and stigma processes. Develop and code a Bayesian meta-regression to pool and
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specimens to estimate historical age structures over the last 150 years. Forecasting Shifts in the Pollination Service Window. The researcher will use Bayesian inference (e.g., Integrated Nested Laplace
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, methodologies, and information derived from Bayesian modeling, data science, cognitive science, and risk analysis. Its primary objective is to create advanced forecasting models, generate meaningful indicators
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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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of the experimental approach will include: Bayesian reconstruction of events on billion-year timescales, determination of optimal embeddings and encodings for protein structures, multiple structural alignments
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expertise/interest in Bayesian methods for addressing measurement error. Ideally PhD within the last 5 years. Advanced level experience with R, desired knowledge of Nimble, Overleaf. Excellent communication
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generation of health data scientists. Areas of expertise include bioinformatics, computational biology, artificial intelligence, network science, Bayesian methods, spatiotemporal methods, visualization
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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