The Department of Health Care Policy at Harvard Medical School is seeking candidates for multiple statistics postdoctoral fellow positions with start dates of Summer/Fall 2018 (flexible). Applicants should have an interest in developing methodological innovations grounded in health care and policy.
The specific positions involve working with statistics faculty on: (1) Bayesian methods and difference-in-difference designs, (2) Machine learning methods for generalizability of observational and randomized studies, (3) Nonparametric methods for difference-in-difference designs, (4) Methods for causal inference using modern optimization, (5) Bayesian methods for comparative effectiveness research, or (6) Multivariate analysis and sample surveys.
Doctoral degree in Statistics, Biostatistics, Computer Science, or related field. Familiarity with causal inference for observational data; strong programming skills, especially for simulation studies; and experience analyzing real data is preferred. Excellent communication and writing skills desired.
Applications are being reviewed on a rolling basis and those received by December 1 receive full consideration
About Harvard Medical School Department of Health Care Policy
HCP is a multidisciplinary research department at the forefront of data science with faculty in medicine, health economics, and statistics.
Harvard University seeks to find, develop, promote, and retain the world’s best scholars. Harvard is an Affirmative Action/Equal Opportunity Employer. Applications from women and minority candidates are strongly encouraged.