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for mathematics across the CWTS Leiden, ARWU, USNews, and QS rankings. In Statistics, the School has research strengths in Bayesian and Monte Carlo Methods, Biostatistics and Ecology, Combinatorics, Data Science
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learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred
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to facilitate the accomplishment of biodiversity conservation research objectives. Develops and writes new proposals to secure contracts for grant-funded research related to biodiversity conservation and the use
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meet the goals and objectives of the department and institution. Minimum Education and/or Training: Bachelor's degree in mathematics, engineering, or computer science with advanced training in
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for: Operational research and combinatorial optimization (e.g., solvers Gurobi, CPLEX, Hexaly) Bayesian optimization, evolutionary algorithms, or hybrid methods Multi-objective and constrained optimization Surrogate
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tenured/tenure-track faculty and nine full-time instructors. Current research areas of the faculty include survival and reliability analysis, Bayesian statistics, latent variable methods, item response
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of economically valuable species and invasive species that may compete with economically valuable species or promote wildfire. Learning Objectives: The postdoctoral fellow will have the opportunity to apply
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with large datasets, with a minimum 200 records, using statistical software packages including SAS, R, SPSS, and STATA. (Required) Demonstrated knowledge of at minimum one general object-oriented
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learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred
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and supervision of postgraduate research students. To develop research objectives and proposals for own or joint research including research funding proposals To attend and or present at conferences