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Onnela Lab Postdoctoral Research Fellow Position Digital Phenotyping / Smartphone Data Research
Harvard University in Cambridge, Massachusetts
 
 
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Date Posted November 08, 2022
Category
Admin-Laboratory and Research
Employment Type
Application Deadline Open until filled
 
 
 
 
 

Details


Title Onnela Lab Postdoctoral Research Fellow Position Digital Phenotyping / Smartphone Data Research
School Harvard T.H. Chan School of Public Health
Department/Area Biostatistics
Position Description
The Onnela Lab in the Department of Biostatistics at the Harvard T.H. Chan School
of Public Health is seeking candidates with a Ph.D. in biostatistics, applied mathematics, computer science, or a related quantitative field for a two-year Postdoctoral Research Fellow position. This position involves developing statistical methods, data analytic tools, and mathematical models for analyzing smartphone data–collected with our high-throughput digital phenotyping platform–in biomedical research cohorts with the goal of establishing more precise and dynamic disease phenotypes. Specifically, this postdoc position will focus on developing statistical methods for the recognition and quantification of human activity data using primarily accelerometer data from smartphones and wearables.
Basic Qualifications
Doctoral degree in statistics, biostatistics, computer science, applied mathematics, or a related quantitative field. Excellent programming skills, as well as strong oral communication and writing skills are required.
Additional Qualifications
Special Instructions
For more information about the lab, please visit https://www.hsph.harvard.edu/onnela-lab/.
Contact Information
Hassan Dawood
Contact Email hdawood@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 2
Maximum Number of References Allowed 5
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