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and research in several areas. These include, but are not limited to: Adversarial location and network interdiction models Adversarial machine learning attacks and defense (e.g., against Bayesian
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-on experience in infectious disease modeling and vaccinology in an applied government setting while learning and practicing Bayesian methods for nowcasting, forecasting, and Rt estimation. You will also develop
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applying customized Bayesian statistical models for wildlife management and monitoring data. Learning how spatial and ecological modeling approaches are used to study wildlife disease spread and management
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limited to mathematical statistics, probability theory, computational statistics, Bayesian modeling, zero-inflated ecological models, spatio-temporal modeling, probabilistic sampling, hierarchical modeling
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Implementing Bayesian networks and uncertainty quantification techniques to account for sensor noise and model confidence limits Designing, training, and fine-tuning computer vision models to extract clinically
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statistical modeling (ideally Bayesian statistics) Proficiency in Fortran, C/C++R, Python, and Matlab. Strong computational skills Strong oral and written communication skills Stipend $5,000.00 – $18,000.00