Postdoctoral Research Position in Statistics and Machine Learning for Aging Research

1 day left

Cambridge, Massachusetts (US)
03 Mar 2017
End of advertisement period
31 Mar 2017
Contract Type
Fixed Term
Full Time

Postdoctoral Research Position in Statistics and Machine Learning for Aging Research

T.H. Chan School of Public Health


Position Description

This is a postdoctoral position focused on developing statistical methods for finding structure in complex systems, working with Jeff Miller in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. The primary application of interest is studying longevity and healthy aging, including specific diseases of aging such as Alzheimer’s and cancer as well as the general mechanisms of aging at the molecular and cellular level. Topics of interest include scalable algorithms for model structure inference, robustness to model misspecification, integrating diverse data, and methods for automated experiments.

Basic Qualifications

Doctoral degree in Statistics/Biostatistics, Computer Science, Computational Biology, or a related field. Expertise in machine learning and Bayesian statistics, including optimization, MCMC, EM, dynamic programming, graphical models, hypothesis tests, etc. Strong programming skills (e.g., in Julia, Python, R, C++). Experience working with large genomics data sets is a plus.

Additional Qualifications Special Instructions Contact Information

Administrative questions regarding this position can be sent to Susan Luvisi at biostat_postdoc@hsph.harvard.edu.

Scientific questions regarding this position can be sent to Jeff Miller at jwmiller@hsph.harvard.edu.

Contact Email biostat_postdoc@hsph.harvard.edu 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, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law.

Minimum Number of References Required 2 Maximum Number of References Allowed 3 Supplemental Questions

Required fields are indicated with an asterisk (*).

Applicant Documents Required Documents

  1. Curriculum Vitae
  2. Cover Letter

Optional Documents