Vacancy ID: PDS002952

Position Summary/Description:

We are searching for a postdoctoral fellow trained in one or more of the following fields: nutrition, epidemiology, population science, biostatistics, or bioinformatics with strong statistical training interested in interdisciplinary research on health using big data. The research will take place at the Carolina Population Center and the postdoctoral fellow will work as part of a collaborative team with expertise in nutrition, human biology, epidemiology, economics, and biostatistics. Ideally, the candidate will have experience in lifecycle research and longitudinal analyses, with experience in one or more of the following: aging research, biomarkers of cognitive impairment and dementia, lifestyle behaviors, or antecedents of human capital outcomes. Our NICHD- and NIA-funded R01 projects use data from the China Health and Nutrition Survey (CHNS) focused on research that spans the lifecycle. The CHNS captures dramatic and rapid modernization- and environment-related changes in individual, household and community environments across the past 30 years for 3,560 three and four generation and 6,080 two generation households and provides high quality data for use by a large scientific and policy community interested in global health trends. There will be ample opportunities for connecting with a wider network of scientists and career development through collaborations across the Carolina Population Center and the Gillings School of Global Public Health. The initial term of appointment is one year, reappointment for a 2nd and 3rd year is likely depending on contributions to the progress of the project.

Education and Experience:

We are seeking a productive, self-driven, and collaborative scientist with a strong background in preferably more than one of the following fields: aging or lifecycle research, epidemiological methods, causal modeling, data science, high dimensional analysis, and experience in interdisciplinary research.

Experience with large datasets, cohort studies, SAS/STATA/R, longitudinal analyses and collaborative team science desired.

Experience with aging research, biomarkers of cognitive impairment and dementia, lifestyle behaviors, or antecedents of human capital outcomes is highly preferred.