The postdoctoral fellow will help address issues related to the measurement of area-level racial bias using online and social media. The fellow will implement analytic approaches using the timing of race-related events to quantify the extent to which changes in area-level racial bias are related to changes in birth outcomes. The project employs an array of methods including rigorous quantitative approaches involving large datasets, machine learning models, analysis of natural experiments, regression methods, and qualitative research methods to accomplish the research objectives. The role of the fellow would be to implement the quantitative analyses and be part of the broader research team to support the machine learning and qualitative work. 

The postdoctoral fellow will gain experience in data management of large data sources, data analysis, manuscript preparation, and grant proposals. The fellow will have opportunities to first author papers and have dedicated time to prepare her/his own grant proposals. The fellow will also have opportunities to attend conferences and will collaborate with the interdisciplinary research collaborative, Big Data for Health Equity (BD4HE), led by Dr. Nguyen. BD4HE is composed of faculty, trainees, and students across various universities in the U.S. committed to advancing research on inequities in health using big data. As a result, the fellow will have a well-rounded and robust preparation for future scientific and career endeavors. People who identify with groups which are under-represented in academia are especially encouraged to apply as a diverse research team with strengthen this work.