In 2021, CDC established a 2-year Public Health Analytics and Modeling Track within the CDC Steven M. Teutsch Prevention Effectiveness Fellowship. This track enhances the use of mathematical modeling, forecasting, and advanced analytics in public health through the recruitment, internal placement, and professional development of doctoral graduates trained in mathematical or analytical fields. Disease modeling is of increasing importance to public health. Using models, researchers can assist in the interpretation of complex epidemiological data, inform risk assessments, forecast future disease activity, and inform intervention planning and impact assessment.

The PE Fellowship is recruiting approximately 10 positions specifically for modeling assignments within various programs at CDC. Applicants must hold a terminal (e.g., PhD, ScD) degree in a mathematical or analytical field such as mathematical modeling, operations research, statistics, computer science, ecology, quantitative epidemiology, or other applied science field. Assignments focus on work related to the following types of modeling; disease transmission models, individual, compartmental, and agent-based models, spatial or network models, simulation and system dynamics modeling, and econometric models. Training will be provided in decision analysis, disease transmission modeling, advanced econometrics, machine learning and big data analysis, data visualization, and key software applications such as R, Python, etc.

All doctoral degree requirements must be completed by 7/21/2023. The starting annual salary is approximately $87,000 plus Federal benefits. The application period is 9/6/2022 to 1/6/2023. The fellowship begins on 8/14/2023 (this date is flexible) with an intensive orientation and training program. Interested individuals should apply now at www.cdc.gov/pef or send an email of inquiry to Dr. Adam Skelton: PEF@cdc.gov. Phone contact is; 404-861-3882. CDC will sponsor suitable visas for non-US citizen candidates.