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 noninvasive cancer detection using high-dimensional genomics data from blood samples (liquid biopsies). 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 accurate and noninvasive early cancer screening with liquid biopsies.
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++). Experience with genomics data is a plus.
Primary author on at least one publication in a leading peer-reviewed journal.
Contact Email: 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.
Internal Number: 9756
About Harvard University
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