Postdoctoral Fellow, Harvard T.H. Chan School of Public Health
Harvard T.H. Chan School of Public Health
This is a two-year 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. The primary focus is on methods for high-dimensional clinical and genetic data to perform dimension reduction, variable selection, cluster analysis, disease subtype discovery+classification, and prediction of disease onset+progression. Models and methods of interest include hierarchical models, latent factorization models, sparse regression, mixture models, machine learning algorithms, and optimal experimental design.
Through a partnership with the Collaborative Center for X-linked Dystonia Parkinsonism (CCXDP) at Massachusetts General Hospital, we have access to longitudinal clinical and genetic data for individuals with a rare genetic disease that leads to dystonia and Parkinson-like symptoms. This postdoctoral position will involve developing methods for and analyzing this data, working with Dr. Miller and the CCXDP team to better understand and develop treatments for this debilitating disease.
Doctoral degree in Biostatistics, Statistics, Computer Science, Applied Math, or a related field. Expertise in Bayesian statistics and machine learning, including hierarchical models, factorization models, sparse regression, mixtures, tree-based methods, MCMC, EM, hypothesis tests, etc. Strong programming skills (e.g., in Julia, Python, R, C++). Experience with high-dimensional categorical/ordinal data is a plus.
Primary author on at least one publication in a leading peer-reviewed journal.
Please also include
- Cover letter, including why you think this position is a good fit for you.
- Sample publications
Application questions regarding this position can be sent to Susan Luvisi at firstname.lastname@example.org.
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.
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