Postdoctoral Research Fellow in Statistical Learning and Biomedical Data Science Harvard University Harvard T.H. Chan School of Public Health

Position Title Postdoctoral Research Fellow in Statistical Learning and Biomedical Data Science School Harvard T.H. Chan School of Public Health Department/Area Biostatistics Position Description The Department of Biostatistics at Harvard TH Chan School of Public Health invites applications for a postdoctoral fellow position focused on developing innovative statistical and machine-learning methods for integrating data and evidence from various types of complex biomedical data (e.g., electronic health record data, claims data, biobanks). Areas of particular interest include (but not limited to): (1) genetic risk prediction for underrepresented populations; (2) distributed statistical inference in clinical research networks; (3) statistical methods for high-dimensional data. The postdoctoral fellow will be supervised by Dr. Rui Duan at Harvard and will work closely with collaborators across multiple institutions. Basic Qualifications 1. Doctoral degree in Biostatistics, Statistics, Computer Science, Epidemiology, Data Science, Engineering, or a related quantitative field. 2. Strong theoretical training in statistics or strong programing skills (R, Python, or C++) are desired. 3. Experience with genomics data is a plus. 4. Excellent communication skills and good track record of writing scientific papers. Special Instructions Application Materials: • a cover letter • a curriculum vitae • one-page research statement • one first author publication • three references Contact Information Rui Duan Contact Email rduan@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, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law. Minimum Number of References Required 3 Maximum Number of References Allowed 5 Supplemental Questions Required fields are indicated with an asterisk (). Applicant Documents* Required Documents Curriculum Vitae Cover Letter Statement of Research Publication Optional Documents Publication 2 Publication 3 If you apply for this position please say you saw it on Medicinoxy Apply

Position

Title Postdoctoral Research Fellow in Statistical Learning and Biomedical Data Science

School Harvard T.H. Chan School of Public Health

Department/Area Biostatistics

Position Description

The Department of Biostatistics at Harvard TH Chan School of Public Health invites applications for a postdoctoral fellow position focused on developing innovative statistical and machine-learning methods for integrating data and evidence from various types of complex biomedical data (e.g., electronic health record data, claims data, biobanks). Areas of particular interest include (but not limited to): (1) genetic risk prediction for underrepresented populations; (2) distributed statistical inference in clinical research networks; (3) statistical methods for high-dimensional data. The postdoctoral fellow will be supervised by Dr. Rui Duan at Harvard and will work closely with collaborators across multiple institutions.

Basic Qualifications

1. Doctoral degree in Biostatistics, Statistics, Computer Science, Epidemiology, Data Science, Engineering, or a related quantitative field.
2. Strong theoretical training in statistics or strong programing skills (R, Python, or C++) are desired.
3. Experience with genomics data is a plus.
4. Excellent communication skills and good track record of writing scientific papers.

Special Instructions

Application Materials:

• a cover letter
• a curriculum vitae
• one-page research statement
• one first author publication
• three references

Contact Information
Rui Duan

Contact Email rduan@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, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.

Minimum Number of References Required 3
Maximum Number of References Allowed 5