Open Positions

Senior Research & Financial Administrator

Reporting to Director of Administration, Senior Research and Financial Administrator (SRFA) provides pre- and post-award grants and general faculty and fellowship financial management for the Department of Statistics. More information about this position, along with application instructions, can be found at https://hr.harvard.edu/search-jobs. Please search for requisition 56659BR. 

Staff Assistant III

Reporting to Director of Administration, staff assistant performs a variety of complex administrative support duties providing front-line operational support to Statistics Department faculty, staff, and students. More information about this position, along with application instructions, can be found at https://hr.harvard.edu/search-jobs. Please search for requisition 56498BR. 

Postdoctoral Position: Assistant Professor Pragya Sur
The Department of Statistics invites applications for a Postdoctoral Fellow with Assistant Professor Pragya Sur. Dr. Sur’s lab focuses on research in high-dimensional statistics and statistical machine learning. Potential research projects include (but are not limited to) developing statistical theory and methods for high-dimensional transfer learning; learning from large-scale (potentially) heterogeneous training data arising from multiple studies/environments/sources; causal inference in high dimensions; high-dimensional Bayesian inference; understanding properties of ensemble learning algorithms used in the analysis of large-scale datasets, e.g. boosting, neural networks, etc. The appointment will be for up to two years with annual renewal based on satisfactory performance and continued availability of funding. A Ph.D. in Statistics, Mathematics, Electrical Engineering, Computer Science, Machine Learning, or a directly related field at time of appointment is required. 
Please visit https://academicpositions.harvard.edu/postings/10891 for more information and to apply.

Teaching Fellows and Course Assistants
We invite applications for TFs and CAs for all 100-level courses, with particular interest in STAT 100, 102, 104, 109, 110, 111, 121a and 121b, 139, and 149. Learn more.

Postdoctoral Position: Murphy Lab
Postdoctoral Fellow at Harvard in RL with Applications in Mobile Health! 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.
Basic qualifications: Ph.D. in Statistics, Computer Science, Operations Research, or Electrical Engineering at time of appointment is required. The appointment will be for up to three years with annual renewal based on satisfactory performance and continued availability of funding.
Instructions: 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). School: Faculty of Arts and Sciences, Department: Statistics

Postdoctoral Positions: General
If you are interested in exploring a postdoctoral opportunity at the Department of Statistics, please apply directly to the faculty member whose research is of interest. See our People page for information on research interests and Faculty websites.


Harvard is 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.