Postdoctoral fellowship at Harvard Biostatistics

The International Society for Bayesian Analysis; Boston (MA); On-site; USD 60,000 - 75,000; Full time

Job summary

A leading research institution offers a postdoctoral fellowship in developing advanced statistical methods for analyzing complex biomedical data. The position emphasizes collaboration with top experts in biostatistics, focusing on innovative models and software tools for significant applications in cancer genomics.

Qualifications

• Advanced expertise in Bayesian statistics and machine learning essential.
• Strong programming skills required.
• Primary author on at least one publication in a leading peer-reviewed journal.

Responsibilities

• Develop statistical methods for biomedical data analysis.
• Work with Dr. Miller and collaborators on cancer genomics.
• Develop software tools for analyzing high-dimensional biomedical data.

Skills

Bayesian statistics

Machine learning

Programming in Julia

Programming in Python

Programming in R

Programming in C++

Education

Doctoral degree in Statistics, Biostatistics, Computer Science, Applied Math, or related field

Job description

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.