Postdoctoral Research Positions in Statistical Methodology for Health Policy
The Department of Health Care Policy (HCP) at Harvard Medical School is seeking candidates for multiple statistics postdoctoral fellow positions with anticipated start dates of Summer/Fall 2018 (flexible). Applicants should have an interest in developing methodological innovations grounded in health care and policy applications.
HCP is a multidisciplinary research department at the forefront of data science with faculty in medicine, health economics, and statistics. The specific positions involve working with statistics faculty on: (1) Bayesian methods and difference-in-difference designs [Laura Hatfield] (2) Machine learning methods for generalizability of observational and randomized studies [Sherri Rose] (3) Nonparametric methods for difference-in-difference designs [Sherri Rose] (4) Methods for causal inference using modern optimization [Jose Zubizarreta] (5) Bayesian methods for comparative effectiveness research [Sharon-Lise Normand] (6) Multivariate analysis and sample surveys [Alan Zaslavsky]
Qualifications:
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.
Additional Information:
Interested individuals should apply once, emphasizing their qualifications for and interest in a specific position or positions. Applications will be considered jointly to identify the best match of candidates to positions. To apply, submit a cover letter describing your research interests, a current CV, and have at least two references send letters to Sarah Chambers at chambers@hcp.med.harvard.edu.
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.