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Field
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Scalable Inference: Develop new algorithms for scalable uncertainty quantification (UQ) and Bayesian inference and apply them to challenging simulation problems. The goal is to produce robust, validated
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datasets. Apply sensitivity analysis, parameter subset selection, and Bayesian inference to improve model identifiability and predictive capability. Implement computational pipelines in Python, MATLAB, and
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service. We welcome applicants from all areas of statistics. Preference will be given to candidates whose research interests overlap with the existing faculty, particularly causal inference, high
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experimental methods. Develop and apply methods for demultiplexing, normalization/QC, effect-size estimation, biological inference, and predictive modeling. Contribute to biological manuscripts and methods
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, surveys, experiments, simulations, Bayesian inference, and advanced quantitative analysis. We are especially interested in courses on the applied use of generative AI, including courses on developing and
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the graduate curriculum, and social sciences scholarship across the school. Examples of topic areas include (but are NOT limited to): models for inference (e.g., SEM/CFA, Bayesian modeling, linear mixed effects
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of electromagnetic wave physics or astrophysics, considered an asset. - Experience with advanced statistics and Bayesian inference, which will be regarded as a plus. Familiarity with compressed sensing and the ability
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in at least one of the following domains: mathematical statistics, machine learning, deep learning, natural language processing, Bayesian inference.
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, to support predictive modelling, deep phenotyping, and real-world evidence generation. Apply and refine causal inference methodologies, such as structural equation modelling and Bayesian approaches, to better
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parameter estimation using Bayesian inference, and/or the exploitation of Machine Learning (ML) based algorithms to reduce false positives caused by human generated interference signals in the observational