Postdoctoral Fellow: Seroepidemiologic methods to study infectious disease transmission in low resource settings

The Francis I. Proctor Foundation at the University of California, San Francisco is seeking a postdoctoral fellow to contribute to seroepidemiologic studies of trachoma and other neglected tropical diseases. The NIH-funded position will focus on computational research in close collaboration with immunology labs at the US Centers for Disease Control and Prevention, the Kenya Medical Research Institute (KEMRI), and potentially others. Our group has multiple, active field studies throughout Africa funded by the NIH and Bill & Melinda Gates Foundation, many of which include geolocated, serological antibody measurements.

The postdoctoral scholar will work closely with Dr. Benjamin Arnold to study infectious disease transmission through antibodies measured in blood, using machine learning and semi-parametric methods. We are embarking on new spatial dimensions of this work, such as using geostatistical models to identify foci of transmission (“hotspots”) and to predict locations of future infection. The postdoc would join an exceptional team at Proctor, with latitude to lead seroepidemiologic analyses at the intersection of disease elimination, integrated serological surveillance, child mortality, and child nutrition. Extensions of the seroepidemiologic research to enteric pathogens, malaria, and vaccine preventable diseases would be possible. The new member of our team will connect into a broad academic network that includes colleagues in Proctor’s infectious disease modeling core (2 other postdocs, currently), UC Berkeley epidemiology/biostatistics, UCSF’s EPPI Center (https://eppicenter.ucsf.edu/), and the NTD modeling consortium (https://www.ntdmodelling.org/).

Ideal applicants will have a PhD and a record of achievement in infectious disease epidemiology, biostatistics, or quantitative biological fields. Applicants must have strong writing and analytical skills, should be adept at programming and data analysis (e.g., R, Python), and should have publication record commensurate with experience. We are particularly interested in applicants who are interested in global health and who have experience in geostatistical modelling approaches in R and/or Google Earth Engine environments. Applicants without experience in geostatistical modeling but who have strong computational skills and an interest in the topic should apply. Experience with machine learning, causal inference methods (e.g., DAGs, structural causal models, doubly-robust estimation approaches), and transparent/reproducible data science tools (e.g., GitHub, R markdown) would be helpful to integrate quickly into our team’s workflow.

This position is located at UCSF in San Francisco (https://proctor.ucsf.edu/), and is available beginning in May 2020 (start date negotiable). We will review applications on a rolling basis through the recruitment period. We are dedicated to mentoring and supporting our postdocs so they succeed in their academic career.

UCSF seeks candidates whose experience, teaching, research, or community service has prepared them to contribute to our commitment to diversity and excellence. The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age or protected veteran status.

Interested applicants should submit a curriculum vitae, a 1-2 page letter that describes their scientific contributions to date and interest in the above areas of research, and contact information for three references, to Dr. Ben Arnold ben.arnold@ucsf.edu. Specific questions regarding this position can be addressed to Dr. Arnold as well.