Pre-Doctoral Fellowship in Development and Urban Economics
Dr Gabriel Kreindler (Harvard Economics Department) is hiring a Pre-Doctoral Fellow to work on projects related to urban transportation topics in developing countries.
This position is ideal for a candidate with exceptional technical skills and motivation. The candidate will collaborate and assist Dr Kreindler at all stages of research, with a focus on technical data processing and data analysis, such as: writing code to automatically process raw GPS traces, collecting new types of mobility data, analysis of large, partially structured data sets, model simulations. An example of the type of project the fellow would work on is measuring the impact of congestion pricing on traffic congestion and commuter welfare (tinyurl.com/y6tq4ykr).
This is a full-time position based at Harvard University for a period of one year with the possibility of renewal for a second year.
This position is ideal for exceptional graduating seniors or masters students with a strong interest in pursuing a Ph.D. in Economics or a related field after acquiring research experience. It will include relevant tasks to provide preparation for graduate work: data cleaning, statistical data analysis, summarizing and writing research results. Other tasks may be included. The fellow will also be an active participant in the Harvard University research community. The fellowship includes opportunities to develop a research agenda in related topics, to attend seminars, and to audit a course (with permission of the instructor).
Salary is $48,000, plus benefits.
Expected start date is September 2019 or earlier.
Basic Qualifications
• Bachelor’s degree in Economics or technical field (e.g. Computer Science).
• Excellent programming skills and proven track-record of learning new languages or tools. Experience in Python strongly preferred. Knowledge of STATA, R, Matlab is a plus.
• Research experience is a strong plus (work as research assistant or independent research)
This opportunity has expired. It was originally published here:
https://academicpositions.harvard.edu/postings/8946