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, Artificial Intelligence , Bayesian Statistics , Big Data , Scientific Machine Learning , Social Sciences , Biomedical Informatics , Causal Inference , Computational Social Science , Data Science and
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of inverse problems, Bayesian learning, and uncertainty quantification. The specific project will be tailored to your expertise and interests; examples include: Efficient inference techniques for high
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tools for nutrition and health method development in causal inference, integration of heterogeneous data sources, uncertainty quantification Work with a wide range of data types, for example dietary
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getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution over the parameter space) without specifying a model nor a prior. Such methods can in principle be applied
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detection model to a more flexible unequal-variance model in a hierarchical Bayesian approach (Lages, 2024). Techniques used: Computational modelling, Bayesian inference, sampling and simulation techniques
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(“propagates”); how it varies among diseases, subtypes, and individuals; how risk factors influence mechanisms. The role holder will work within a common Bayesian inference framework enabling quantification
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beginning August 2026. Visit https://sc.fsu.edu , for more information. The successful candidate is expected to develop an interdisciplinary research group with a focus on Bayesian inference or inverse
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environmental factors such as fluctuating wind speeds and saltwater exposure. Using advanced statistical and machine learning techniques, including Bayesian inference and stochastic modelling, the project will
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-informed machine learning. The ideal candidate will have a strong background in developing and integrating probabilistic graphical models, Bayesian networks, causal inference, Markov random fields, hidden
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for learning about models from data, 2) incorporation of expert knowledge in model building through Bayesian prior elicitation, and 3) develop new methods for identification of conflicts in different parts