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Postdoctoral Fellow in Statistics
Harvard University in the Cambridge, Massachusetts
 
 
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Date Posted March 07, 2019
Category
Postdoctoral-Mathematics

Employment Type Full-time
Application Deadline Open until filled
 
 
 
 
 
Title Postdoctoral Fellow in Statistics
School Faculty of Arts and Sciences
Department/Area Statistics
Position Description

Join us as a postdoctoral fellow in Professor Susan Murphy’s Statistical Reinforcement Learning Group. Our research concerns sequential decision making in mobile health, including experimental design and reinforcement learning algorithms. We combine statistical methods with algorithms from reinforcement learning so as to provide inferential tools. The successful applicant will be expected to develop an innovative research program in sequential decision making. Our lab is involved in a number of mobile health studies in obesity, cardiac health, physical activity, mental illness and substance abuse across the world.

The appointment will be for up to three years with annual renewal based on satisfactory performance and continued availability of funding.

Basic Qualifications

Ph.D. in Statistics, Computer Science, Operations Research, or Electrical Engineering at time of appointment is required.

Additional Qualifications  
Special Instructions

To apply for this position, send application materials; curriculum vitae, statement of research interests, and names and contact information for three references to Research Coordinator Jessamyn Jackson (jessjackson@fas.harvard.edu).

Contact Information

Jessamyn Jackson 
Department of Statistics
Science Center #316
1 Oxford Street
Cambridge, MA 02138

Contact Email jessjackson@fas.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  
Maximum Number of References Allowed
 
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