Postdoctoral Research Fellow, Harvard T.H. Chan School of Public Health
- Employer
- HARVARD UNIVERSITY
- Location
- Massachusetts, United States
- Closing date
- 17 Sep 2019
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- Academic Discipline
- Biological Sciences, Computer Science, Engineering & Technology, Clinical, Pre-clinical & Health, Life sciences, Other Health & Social Care
- Job Type
- Academic Posts, Research Fellowships, Postdocs
- Contract Type
- Permanent
- Hours
- Full Time
School
Harvard T.H. Chan School of Public Health
Department/Area
Nutrition
Position Description
Harvard T.H. Chan School of Public Health (HSPH) invites applications for a postdoctoral research fellow who will work on statistical methods development for multivariate spatial data and high-dimensional multivariate microbiome sequencing data. Under the joint supervision of Drs. Kyu Ha Lee (Nutrition, HSPH), Jacqueline Starr (Forsyth Institute), and Brent Coull (Biostatistics, HSPH). The initial appointment is for one year, with a possible extension for a second year. The position is available immediately, with negotiable start date.
Basic Qualifications
Doctoral degree in biostatistics, statistics, computational biology, or related quantitative fields. Strong research experience in Bayesian modeling, Markov chain Monte Carlo methods is preferred. Also relevant would be experience with spatial statistical modeling or multivariate methods development. Excellent programing skills in R, C/C++ and/or Fortran as well as strong communication and writing skills are required.
Special Instructions
To apply, please send your CV, research statement, and contact information for three references to Dr. Kyu Ha Lee (klee@hsph.harvard.edu) with the subject line “Biostatistics Postdoc (Drs. Lee and Starr)”
Contact Information
Patrice Brown
665 Huntington Ave
Bldg 2 room 305
Boston, MA 02115
Contact Email
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.
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