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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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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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: Experience with surrogacy analyses using regression model and/or meta-analytic model including Bayesian methods and knowledge of continuous glucose monitoring data (or any digitally derived endpoint data
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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