Evidence for Action (E4A), a program of the Robert Wood Johnson Foundation, awards grants to support innovative, rigorous research on how systems, programs, and policies influence population health and health inequalities. The E4A National Program Office reviews research proposals and recommends the most rigorous and high-impact proposals for funding. Numerous methodological challenges arise in efforts to field rigorous, cost-effective, and convincing research in E4A’s priority areas. The National Program Office is launching an internal initiative to identify these issues, provide relevant evidence and tools to overcome these challenges, and spark attention to the challenges from the broader community of quantitative researchers. UCSF seeks to hire a postdoctoral fellow to play a key role in this initiative. Competitive candidates should have training and interest in both quantitative research methods (e.g., epidemiology, sociology, or economics) and multilevel influences on health and health inequalities, including social, behavioral, and institutional risk factors. Specific projects will be prioritized on an ongoing basis by the NPO and National Advisory Committee but may include: Writing manuscripts or developing teaching tools to bridge disciplinary differences in approach to causal inference Empirical research on major sources of bias in common approaches to evaluating determinants of population health and health inequalities Simulation studies to evaluate the performance of recently proposed methodological advances when applied to E4A’s priority research areas in population health or health inequalities The E4A National Program Office is housed in the Center for Health and Community at the University of California, San Francisco. The postdoctoral fellow will work with NPO leadership and most closely with Maria Glymour, Associate Director for the NPO. Required: PhD or equivalent training in epidemiology, sociology, or a related discipline Experience with quantitative research, including expertise in statistical software (SAS, Stata, or R) Familiarity with quasi-experimental methods, such as difference-in-difference, regression discontinuity, or instrumental variables Strong writing skills, ideally for technical, applied, or non-research audiences Applicants may apply to Maria Glymour at email@example.com.