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Postdoctoral Fellow - Harvard Kennedy School/IQSS
Location: Cambridge, MA/Boston, MA
Application deadline: Open until filled
We are looking for a collaborative, enthusiastic, and curious social scientist with strong quantitative skills and expertise in voting data to join us for a 1-year postdoctoral fellowship with joint affiliations at Harvard University’s Shorenstein Center on the Media, Politics, and Public Policy, and Institute for Quantitative Social Science (IQSS), both based Cambridge, MA. Affiliation with the Network Science Institute at Northeastern University could also be arranged. The fellow will work with Matthew Baum and Maya Sen at the Harvard Kennedy School, Ryan Enos at the Harvard Department of Government, and David Lazer at Northeastern University’s Network Science Institute. The fellow will work on quantitative analyses of state- and national-level voter registration data, helping to oversee a project aimed at rigorously investigating the nature and extent of in-person voter fraud (e.g., duplicate registrations) in the United States. The ideal candidates will have a PhD in a social science with strong statistical and programming skills (e.g., familiarity with SQL, R, and Python or the equivalent). The position is fixed-term for one year. Competitive compensation will be offered.
== Responsibilities ==
* Oversee data collection, clean-up, and analyses
* Work with PI’s on drafting scholarly papers, policy reports, and press releases describing results and their implications
== Qualifications ==
* Completed PhD in social science, prior to joining
* Superior statistical, analytical and data management skills
(R, SQL, Python or the equivalent)
* Background in machine learning is a plus, but not required
* Expertise managing large, diverse, and “messy” datasets
* Capacity for and strong interest in producing publishable research papers is a must
* Excellent communication skills, both written and verbal
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.
== To apply ==
Candidates should submit the following materials electronically to Jessica Colarossi ([log in to unmask]). *Email single PDF file that includes*
1. Statement of interest describing relevant background and skill
3. The name and contact information for three references
(one reference should be your doctoral advisor)
4. Letters of recommendation will only be solicited from finalists
5. Publications or other writing samples