The International Society for Bayesian Analysis
Postdoctoral fellowship at Harvard Biostatistics
The International Society for Bayesian Analysis, Boston, Massachusetts, us, 02298
Postdoctoral fellowship at Harvard Biostatistics
Oct 26, 2022 Postdoctoral fellowship with Jeff Miller at Harvard Biostatistics 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. 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++). Experience with genomics data is a plus. Primary author on at least one publication in a leading peer-reviewed journal.
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Oct 26, 2022 Postdoctoral fellowship with Jeff Miller at Harvard Biostatistics 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. 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++). Experience with genomics data is a plus. Primary author on at least one publication in a leading peer-reviewed journal.
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