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Position TitlePostdoctoral Research Fellow
The Department of Biostatistics at Columbia University is one of the premier biostatistics departments in the nation. The department has strong collaborative connections within the Mailman School of Public Health (MSPH), the Columbia University Medical Center and the Department of Statistics at Columbia University.
Dr. Linda Valeri in the Department of Biostatistics at Columbia University Mailman School of Public Health is seeking a Postdoctoral Research Fellow. The position is available immediately. The one-year position can be extended to additionally two years on the basis of performance, evaluated at the end of each year. This position will provide the opportunity to carry out causal inference and machine learning research in either or both of two collaborative avenues: analyses of environmental mixtures in the context of Bangladeshi and American perinatal and adult intergenerational cohorts in collaboration with the Department of Environmental Health Sciences at Columbia University and Harvard University, and/or the analysis of mobile passive (GPS, call/text logs, sleep data) and active (surveys) data streams in collaboration with the New York Psychiatric Institute, NY, the Departments of Psychiatry at Columbia University and Harvard University, and McLean Hospital, Belmont, MA. The broad goal of the successful candidate will be the development of blended machine learning and causal inference approaches and automated software. The approaches will be applied to harness exposomic data and mobile health data to investigate the joint causal effects of environmental and behavioral factors over time to inform policy on environmental mixtures and to discover behavioral targets of treatment in psychosis. The postdoctoral fellow will have the opportunity to collaborate with scientists across fields and across domestic and international research institutions.
Doctoral degree in Biostatistics, Computer Science, Statistics, or related field; experience and proficiency in Linux/Unix command line and research environments; working knowledge of R, in addition to other scripting environments appropriate for scientific data management (Python, shell, etc.); excellence in research, communication, and collaboration skills, as evidenced by publication record. Experience in causal inference, machine learning (both Bayesian and frequentist paradigms), and software development is highly considered.