We seek highly skilled and motivated individuals to work as full-time Pre-Doctoral Fellows with Professor Jonathan Colmer and Professor Jay Shimshack for a period of two years. The successful candidate will also work closely with John Voorheis, Principal Economist at the U.S. Census Bureau and support partnerships between Colmer, Shimshack, and the U.S. Census Bureau.  Projects are expected to relate to environmental inequality, regulation, and policy interventions to increase environmental resilience and sustainability. The appointment will start in June 2025.

The successful candidate will receive an excellent opportunity to gain exposure to and training in a broad range of research topics and methodologies before pursuing a PhD in Economics. You will receive first-hand experience conducting cutting-edge economics research in the field of environmental and energy economics and applied microeconomics more broadly. The successful candidate will develop advanced programming and data analysis skills, as well as opportunities to work with confidential microdata. Day-to-day tasks will focus on data cleaning and organization, writing code and documentation, and data analysis. Other tasks such as conducting literature reviews and writing policy briefs may be assigned according to need and interest.

As part of your development as a researcher you will be part of a community of scholars, including other pre-docs, learning and pursuing research together. You will participate in group research meetings, receive academic advising to prepare you for your PhD career, and will have opportunities to attend research seminars, interact with other pre-docs, graduate students, faculty members, visiting experts, and external collaborators.

The ideal candidate will:

Have completed an undergraduate degree in Economics, Mathematics, or Computer Science.Be able to meet the qualifications for Special Sworn Status, or have Special Sworn Status.Have a quantitative background with strong programming skills in statistical analysis software (e.g., Stata, R, SAS, Python).Be able to work efficiently and independently to solve problems and manage multiple tasks.Have excellent communication, time management, and organization skills.Have interest in pursuing a PhD in Economics or a related field.

Intellectual curiosity, internal drive, and a desire to do things well can compensate for most technical qualifications.

To apply, visit the UVA job board at https://jobs.virginia.edu/us/en and search Posting Number R0067292, complete the application and include the following documents:

A cover letter summarizing interest and qualificationsA CV/resume (please include GPA and relevant courses on CV/resume)A sample of code in Stata or R (can be anything you have worked on for coursework or other RA work)Contact information for 3 references

The position will remain open until filled. Salary is highly competitive. This is a non-exempt level, benefited position.  For more information on the benefits at UVA, visit hr.virginia.edu/benefits.  This is a restricted position for a term of two years. This position is located in Charlottesville, VA. 

The University will perform background checks on all new hires prior to employment. For questions about the application process, please contact Jessica Russo, Senior Academic Recruiter, at sxv9zv@virginia.edu.

For more information about UVA and the Charlottesville community please see http://www.virginia.edu/life/charlottesville and https://embarkcva.com/.

The University of Virginia, including the UVA Health System which represents the UVA Medical Center, Schools of Medicine and Nursing, UVA Physician’s Group and the Claude Moore Health Sciences Library, are fundamentally committed to the diversity of our faculty and staff.  We believe diversity is excellence expressing itself through every person's perspectives and lived experiences.  We are equal opportunity and affirmative action employers. All qualified applicants will receive consideration for employment without regard to age, color, disability, gender identity or expression, marital status, national or ethnic origin, political affiliation, race, religion, sex, pregnancy, sexual orientation, veteran or military status, and family medical or genetic information.