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) Familiarity with mathematical programming solvers (e.g., Gurobi, CPLEX) or probabilistic/Bayesian computing frameworks (e.g., Stan, PyMC) Ability to formulate high-impact, novel and well-defined research
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scientific insights into structured, prioritized research recommendations. Deepen understanding of research-priority frameworks used within federal public health agencies. Strengthen scientific communication
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emerging events; research biology-related AI capabilities and their implications for biosecurity; and contribute to frameworks for responsible evaluation and use of AI, including scientific validity
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engage in research and evaluation projects under the guidance of experienced mentors, applying implementation science frameworks to assess cancer prevention, screening, and survivorship interventions
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potential public health impacts of chemical exposures. Learn ATSDR methods and frameworks for conducting environmental public health assessments and risk evaluations. Gain experience creating data
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communication approaches that support populations at greatest risk of nutrition insecurity. Building skills in implementation science by learning to apply evidence-based frameworks to program design
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-grading frameworks (e.g., GRADE). Experience with systematic review software (e.g., Covidence, Rayyan) and statistical analysis tools. Strong foundational knowledge of clinical terminology, particularly
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insights to human operators Implementing frameworks for continuous model monitoring, retraining, versioning, and deployment to ensure the digital twin and associated AI models adapt to degradation or changes
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vital role in CDC’s public health mission, ensuring all laboratory activities meet the highest scientific standards. The laboratory quality management system (LQMS) provides a structured framework for