This position assists with a large-scale longitudinal survey with participants surveyed in six metropolitan areas across three countries (USA, Italy, China) on a near daily basis over about one year, examining human worry about, awareness of, and adaptation to air pollution. As this is a complex study design, this position will involve advanced knowledge of the following topics: R programming, pollution data modeling, GIS data analysis, statistical analysis, machine learning and training in behavioral science. The position is ideal for someone considering a research career in quantitative social science and interested in applying research to address social problems (e.g. climate change and poverty or inequality) who already has considerable experience with data analysis. This position also includes opportunities to learn and develop research skills and receive professional mentorship (e.g., in preparation for applying to a PhD).
- Analyze pollution data models, GIS data, social media data and longitudinal survey data
- Organizational support for the project, including team meeting notes, interview notes, and managing project timeline
- Conduct literature reviews on existing research in the field of adaptation to air pollution
- R programming
- Experience with pollution data modeling
- Experience with GIS data analysis
- Statistical analysis
- Machine learning
- Familiarity with social media or text analysis
- Strong understanding of a social science perspective on environmental issues (e.g. political science, economics, psychology, sociology)
Be advised that you will be contacted only if there is further interest in your application. Your candidate dashboard may not display status updates for this requisition.
Princeton University is an and all qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status, or any other characteristic protected by law.
Standard Weekly Hours:
Eligible for Overtime:
Essential Services Personnel (see policy for detail):
Estimated Appointment End Date:
Physical Capacity Exam Required:
Valid Driver's License Required: