Position Description: This is a postdoctoral position developing statistical methods for finding patterns in complex biomedical data, working with Jeff Miller in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. Models and methods of interest include hierarchical regression models, latent factorization models, nonparametric Bayesian models, models for sequential data, mixture models, machine learning algorithms, and robustness to model misspecification. This postdoctoral position will involve working with Dr. Miller and collaborators to develop statistical methods and software tools for analyzing high-dimensional biomedical data from cancer genomics and clinical applications.
Basic Qualifications: Doctoral degree in Statistics, Biostatistics, Computer Science, Applied Math, or a related field. Advanced expertise in Bayesian statistics and machine learning is essential. Strong programming skills are required (e.g., in Julia, Python, R, C++). Primary author on at least one publication in a leading peer-reviewed journal.
Special Instructions: Please also include: - Cover letter, including why you think this position is a good fit for you. - CV - Sample publications
Contact Information: Trevor Bierig
Contact Email:
biostat_postdoc@hsph.harvard.edu
Equal Opportunity Employer: We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.
Minimum Number of References Required: 2
Maximum Number of References Allowed: 4
Supplemental Questions: Required fields are indicated with an asterisk (*).
