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transferring learning from other geographic regions and data types, machine learning methods, Bayesian inference and interrogation theory. The post may involve travel to Iceland and Italy in support of your work
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theories from tractable models (probabilistic circuits) and Bayesian statistics to tackle the reliability of machine learning models, touching topics such as uncertainty quantification in large-scale models
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presentation of analysis results. The ability to work with large and complex datasets. Excellent spoken and written English skills. Experience in machine learning, predictive modeling, and/or Bayesian methods
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. We are interested in candidates with research interests in causal inference or Bayesian methodology, and we also welcome strong applicants from the broader fields of statistics and machine learning
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interests, which include scalable Bayesian methods, spatial statistics, and statistical methods for massive or complex data, in general, and high-dimensional data analysis, statistical machine learning
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plasma-material interactions in fusion energy systems. You will also advance knowledge of key AI methods such as deep learning, operator learning, and Bayesian optimization, and apply it to develop next
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Bayesian hierarchical modeling with applications in transportation, urban planning, environmental science, public health, and computational social science. The incumbent will teach the standard teaching load
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, certification, and/or registration. Additional Qualifications Working knowledge and experience with R, Stan, Nimble, or other relevant analytical software. Knowledgeable of Bayesian statistical methods, numerical
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Professor of Statistics Tenure-Track or Tenured Faculty Opening in the Department of Statistics at Rice University https://statistics.rice.edu https://engineering.rice.edu/ https://www.rice.edu/ Position