Post-Doctoral Fellow


University of North Carolina


Chapel Hill, NC


Post-Doctoral Fellow in Machine Learning for Environmental Health Sciences

The Department of Environmental Sciences and Engineering at the University of North Carolina, Chapel Hill, is seeking a Postdoctoral Fellow in Machine Learning in Environmental Health Sciences.

The Fellow will work closely with Dr. Jackie MacDonald Gibson to develop machine-learned models for uncovering drivers of environmental and public health risks and for predicting the benefits of interventions. Dr. MacDonald Gibson’s lab focuses on strengthening the science of environmental risk assessment and decision-making. Application areas include “mining” existing data to develop predictive models of GenX and other emerging contaminants in private wells; uncovering key risk factors for lead in drinking water; and identifying how environmental exposures interact with genetic, socioeconomic, and behavioral factors to influence health.

The Post-Doctoral Fellow will have the opportunity to conduct field research in communities concerned about their drinking water quality. Field research opportunities will include collection of environmental (water, soil, and dust samples) and human biological samples in collaboration with trained medical specialists. This research also offers opportunities to interact directly with officials from the NC Department of Environmental Quality and the NC Division of Public Health. The Fellow also will have the opportunity to collaborate with Dr. MacDonald Gibson in the development of a course on machine learning in environmental health sciences. In addition, the Fellow will help to mentor 10 graduate and several undergraduate students working in Dr. MacDonald Gibson’s lab.

The Post-Doctoral Fellow will initially be appointed for a two-year period, with the potential for renewal. The salary will be commensurate with NIH post-doctoral fellowship guidelines.

Applicants must hold a PhD in a relevant field, including but not limited to environmental science and engineering, statistics, computer science, or public health. Demonstrated skills in computational methods and quantitative analytics are required. Previous experience with machine learning, especially learning of Bayesian networks, is highly desirable. Excellent written and oral communication skills also are required.

To apply, please visit the following web link:

Contact Name

Jacqueline MacDonald Gibson

Contact E-mail