HARVARD UNIVERSITY

Postdoctoral Fellow

Location
Cambridge, Massachusetts (US)
Posted
04 Apr 2017
End of advertisement period
02 May 2017
Ref
7535
Academic Discipline
Arts & Humanities, Social Sciences
Contract Type
Fixed Term
Hours
Full Time

Postdoctoral Fellow
Faculty of Arts and Sciences
The Crowd Innovation Lab at the Institute for Quantitative Social Science Position Description

Seeking Postdoctoral Fellow at the Crowd Innovation Lab (www.crowdinnovationlab.org) at the Institute for Quantitative Social Science (IQSS) at Harvard University

The Crowd Innovation Lab (CIL) is launching a new joint effort between Harvard Business School, Harvard Medical School, and the Harvard School of Engineering and Applied Sciences focused on technology commercialization and translation. The purpose of this effort is to generate new academic knowledge on how scientists approach the process of commercialization and, using these insights, build new tools and programs to improve this process. Using a combination of field experiments, micro-level data, and online platforms we intend to run a series of studies on how to best design systems to turn innovative scientific ideas into commercially viable ventures and products.

The Crowd Innovation Lab is seeking a Postdoctoral Fellow with strong empirical analysis skills and interests in innovation, online platforms, and social networks. Under the direction of Professors Karim Lakhani (HBS), Eva Guinan (HMS), Chiara Farronato (HBS), and Rembrand Koning (HBS), the Fellow will build, implement, and analyze a series of field experiments, surveys, and databases that will help researchers and policy makers understand how to improve the process of commercialization.

The research will involve the following tasks:

  • Collect and analyze data on the state of technology development and commercialization.
  • Site Visits to technology labs to meet and survey scientists and engineers.
  • Design, implement, and analyze surveys and experiments to identify civilian uses of novel technologies, and to evaluate the best mechanisms to transfer public and government technologies to the private sector. Surveys and experiments will be implemented in the context of a new Harvard-led Massive Open Online Course (MOOC) focused on commercialization process improvements. The MOOC will be run and operated by HarvardX (harvardx.harvard.edu).
  • Literature reviews for curriculum development and HBS case writing.

Basic Qualifications

Qualifications: 

  • Advanced knowledge and training in statistical/econometric methods.
  • Advanced programming skills with experience in the full pipeline of statistical computing from data collection and sanitation to fitting statistical models.
  • Knowledge of survey and experimental design
  • Proficiency with Python and R. Stata, SQL, Matlab, and Javascript are a plus.
  • Ability to collaborate within a team.
  • Interest in academic research on innovation, science and technology transfer.
  • Must hold Ph.D. in economics, statistics, business, sociology or a related quantitative social science field. PLEASE NOTE: If you have obtained your Ph.D. in the past 12 months you must be able to provide a certificate of completion from the degree-granting institution OR a letter from the institute‚Äôs registrar stating all requirements for the degree have been successfully completed and should verify the date the degree has been conferred. No exceptions.
  • Must be willing to travel
  • We welcome candidates who possess a doctorate with professional backgrounds or industry experience in business analytics or commercialization

Additional Qualifications Special Instructions

HOW TO APPLY:

Interested applicants should send their cv and a cover letter describing their motivation and fit for the position to Kate Powell at kate_powell@fas.harvard.edu

Contact Information

Kate Powell

Contact Email kate_powell@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, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law.

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