Postdoctoral Research Associate in Rural Maternal & Perinatal Health at University of South Carolina

The Arnold School of Public Health at the University of South Carolina is seeking applications for a Postdoctoral Research Associate to join an NIH U01-funded project focused on advancing rural maternal and perinatal health. This position is part of the NIH-funded SBE Consortium, providing opportunities for collaboration with investigators across multiple institutions. The research team at USC includes experts in maternal health services, health policy, telehealth, OB/GYN, nursing, perinatal epidemiology, rural health advocacy, health psychology, big data, and machine learning. The successful candidate will be expected to lead at least two peer-reviewed publications annually, contribute to collaborative manuscripts and national presentations, and engage in interdisciplinary research spanning clinical, policy, behavioral, and data science domains. The role also involves developing detailed workplans, presenting research at conferences, supervising students, and contributing to grant proposals. Applicants should have a doctoral degree in a relevant field such as health services research, statistics, economics, psychology, computer science, public policy, or public health. Preferred qualifications include experience in maternal health or telehealth, quantitative methodology, and proficiency in analysis software such as SAS, R, Stata, or Python. The position is full-time for up to two years, with an initial appointment of at least one year and the possibility of reappointment based on performance and funding. Salary is commensurate with qualifications and experience, and the position includes a comprehensive benefits package. Applications are accepted online until April 11, 2026. For more information or inquiries, contact Ms. Janie Godbold at [email protected] and reference #NIHU01Postdoc. Keywords: rural maternal health, perinatal health, public health, health services research, epidemiology, health policy, telehealth, big data, machine learning.