School : Harvard T.H. Chan School of Public Health
Skills, Experience, Qualifications, If you have the right match for this opportunity, then make sure to apply today.
Department / Area : Biostatistics
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.
Additional Qualifications
Please also include :
- Cover letter, including why you think this position is a good fit for you.
- Sample publications
Contact Information
Contact Email
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
Maximum Number of References Allowed
Keywords
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