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Postdoc Position in Bayesian Statistics
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The Departments of Biostatistics at Columbia University is the premier biostatistics department in the nation. The department has strong collaborative connections with other departments at Columbia University and universities in the New York area.
The Department of Biostatistics at Columbia University is seeking a full-time Postdoctoral Fellow specializing in Bayesian modeling and computing for an NIH-funded research project. The Postdoctoral Fellow will be co-supervised by Dr. Qixuan Chen from the Department of Biostatistics and Dr. Andrew Gelman from the Department of Statistics.
The Postdoctoral Fellow will focus on developing innovative statistical methods and open-source tools in Bayesian modeling, specifically for analyzing data from series dilution assays, such as the Enzyme-Linked Immunosorbent Assay (ELISA). Since its introduction in the 1970s, ELISA has become an essential technique for detecting and quantifying proteins in various laboratory settings. Over the past decade, an average of approximately 15,000 publications per year have been indexed in PUBMED using the search term "ELISA".
The research will address key challenges including measurement error, model contamination, spatial correlations, and detection limits. Beyond methodological development, the Postdoctoral Fellow will collaborate with researchers in environmental health sciences to implement these advancements in practical workflows, facilitating their adoption in laboratory settings. This opportunity offers the Postdoctoral Fellow hands-on experience in implementation science, bridging cutting-edge statistical techniques with real-world applications.
This is a 2-year training position in the Department of Biostatistics (Columbia University Mailman School of Public Health), with possibility of extension subject to satisfactory performance. The Postdoctoral Fellow will get opportunities to regularly interact and possibly collaborate with other members within the Department of Biostatistics as well as the Department of Statistics.
We are particularly interested in candidates with a Ph.D. in Biostatistics or Statistics, who have strong research experience in Bayesian modeling and computing. Preference will be given to candidates with a demonstrated interest in implementation science, specifically in translating research into practical applications to advance scientific knowledge and practice.