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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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trials, multiple endpoints, adaptive designs, Bayesian design and analysis methods, estimands, meta-analyses, benefit-risk analyses, subgroup analyses, biosimilars, patient experience data, bioequivalence
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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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present that were not as well understood when the public health assessment or health consultation was completed. ATSDR’s most important contribution to this process is to apply updated science to identify
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). Learning Objectives: Under the guidance of mentors, you will: Learn how to support scientific evidence reviews, coordination, and/or communication activities for future updates to the Physical Activity
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; surveillance-data analysis; and scientific manuscript development for peer-reviewed publication. Gain experience with updates to DNPAO’s interactive, web-based data portal that displays Division surveillance
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Office of Science. With mentor guidance, you will also gain experience in recommending approaches for regularly reviewing and updating research priorities in response to evolving challenges. Overall
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experience presenting fellowship activities, project updates, or lessons learned at Section, Branch, or other internal meetings at least once per year. Mentor(s): The mentor for this opportunity is Amy Helene