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for inclusion in regulatory review workflows. Learning Objectives: By the end of this appointment, you will have developed the skillsets to: Articulate the regulatory framework governing the use of surrogate
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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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spray chambers and LI-COR gas-exchange systems, as well as opportunities for managing field, growth-chamber, and greenhouse studies to investigate weed control in cropping systems. Learning Objectives
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to provide experiential learning rather than fulfill essential agency functions. Learning Objectives: This 1-year fellowship, with a possible extension as funding permits, may provide the following mentor
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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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other chronic diseases. An important component of a public health response to address these behaviors is surveillance. Surveillance is used to track the behaviors as well as their environmental and
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on wildlife disease impacts in ungulates around the Greater Yellowstone Ecosystem. Learning Objectives: Through this mentored research experience, you will expand your knowledge of wildlife disease ecology
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findings to technical and non-technical audiences. Learning Objectives: Under the guidance of a mentor, you will: Develop skills in epidemiologic and statistical analysis through mentored application
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-performance computing (HPC). The objective of these fellowships is to facilitate cross-disciplinary, cross-location research through collaborative research on problems of interest to each applicant and amenable
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-performance computing (HPC). The objective of these fellowships is to facilitate cross-disciplinary, cross-location research through collaborative research on problems of interest to each applicant and amenable