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Open Postdoctoral position, faculty mentor David Rehkopf

Important Info

Faculty Sponsor (Last, First Name): 
Rehkopf, David
Other Mentor(s) if Applicable: 
Mathew Kiang
Stanford Departments and Centers: 
Epidemiology and Population Health
Medicine: Primary Care and Population Health
Postdoc Appointment Term: 
The fellowship is a one-year appointment, with the option to extend by mutual arrangement.
Appointment Start Date: 
September 1, 2021; start date negotiable

The Center for Population Health Sciences (PHS) is seeking a postdoctoral research fellow to collaborate on a portfolio of pressing and time sensitive community-driven health equity research projects. This opportunity is ideal for someone who is interested in both an academic research track and doing work that is community driven to have an immediate impact on public health practice and health equity.
 
PHS works in collaboration with diverse disciplines and sectors to leverage population level data to improve population health and health equity through addressing social determinants of health. Toward this end, we are partnering with California departments of public health and other governmental and non-profit community organizations on several applied research projects that focus on using novel data sources to understand the effects of social policy and public health programs on health overall and on social inequalities in health in particular. The fellow will collaborate with Professors David Rehkopf (PHS Faculty Co-Director) and Mathew Kiang on several projects related to the following topics(60%):
Analyzing regional health inequalities in rural low income counties in California, utilizing the analysis to inform policy and practice.
Developing a framework and metrics that California departments of public health can use to measure their progress toward health and racial equity.
Assessing the impact of COVID-19 on preventive screenings in rural areas of California
Quantitative support for qualitative community health assessments
Addressing generalizability and transportability of data for inference
 
In addition, the fellow will have the opportunity to propose and execute their own research questions (40%). We expect the public health department projects will form the basis of their independent projects, but that there is substantial freedom to pursue projects that overlap with the fellow’s own research interests.
 
The fellowship will support development and training in two broad areas. First, the fellow will have the opportunity to engage with faculty from across the University, including in statistics and sociology, to inform their research approach to the applied questions, including methodological innovation required to appropriately answer applied questions. Secondly, the fellow will gain on the ground experience in working with public health departments to address their most pressing data analytic needs.

Required Qualifications: 
  • Doctoral degree
  • Demonstrated interest in advancing health equity
  • Experience utilizing different types of large scale datasets
  • Strong written and interpersonal communication skills
  • Strong quantitative skills
  • Strong training in quantitative epidemiologic methods (e.g., causal inference methods, Bayesian methods)
  • Experience using R required (experience with other software like SAS, Stata is helpful as well)
  • Knowledge of the social determinants of health and/or public/population health principles and concepts desired
Required Application Materials: 

Interested applicants should submit the following materials: 

  • Curriculum vitae;
  • Cover letter describing research interests to be pursued during training. If you have experience successfully collaborating with community partners, please note this in your letter.

We will request a sample publication/first-author manuscript with related analysis code and references later during the recruitment process.

 

Stanford is an equal opportunity employer and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by law.