[ES_JOBS_NET] Postdoctoral Researcher - NO2 land-use regression modeling in Dakar, Senegal

[Pusede, Sally Ellen (sep6a)]

Urban Air Pollution, Colonial Policies, and their Legacies on the Poor - Nitrogen Dioxide Land-Use Regression Modeling in Dakar, Senegal

The University of Virginia’s recently launched Pan-University Environmental Resilience Institute (ERI) is focused on exploring the impact of rapid environmental and social changes on a planetary scale and supports pioneering work that integrates knowledge about natural systems, technology, and human behavior to inform choices about our future. Leveraging strengths across the university in environmental science & engineering, social sciences, business, law, humanities, art, and design, the Institute supports transdisciplinary research projects with a strong emphasis on actionable science that can be translated to decision-makers and stakeholders.

The Institute’s new CoLab, Urban Air Pollution, Colonial Policies, and their Legacies on the Poor in Dakar, Senegal, seeks to hire a postdoctoral research associate to collaborate with our interdisciplinary and international team studying urban pollution in Dakar. The associate will be responsible for developing land-use regression modeling of nitrogen dioxide (NO2) across Dakar. To build the model, the associate will work with colleagues at UVA and Cheikh Anta Diop University to enhance the spatial resolution of NO2 surface measurements currently underway. The associate can be involved in data collection at various levels depending on interest, including use of a Pandora spectrometer and vehicle-based NO2 measurements. In addition, the associate will use observations from the recently-launched TROPOMI. To investigate drivers and impacts of Dakar’s NO2 pollution, the associate will work with CoLab members with economic, policy, historical, and environmental justice experience.

QUALIFICATION REQUIREMENTS: A Ph.D. in Geography, Environmental Science, Engineering, or a related field is required. Experience with land-use regression or other relevant geospatial techniques is required and past work with satellite datasets is preferred.

APPLICATION PROCEDURE: Apply online at https://uva.wd1.myworkdayjobs.com/en-US/UVAJobs/job/Charlottesville-VA/REsearch-Associate-in-Environmental-Sciences_R0008030 and attach a curriculum vitae, a brief cover letter describing geospatial, satellite remote sensing, and/or land-use regression experience, and three references with contact information.  While not required, please indicate any experience collecting field measurements.

APPLICATION DEADLINE: Review of applications will begin on July 20, but the position will remain open until filled.

This is a one-year appointment; however, the appointment may be renewed for two additional one-year increments, contingent upon available funding and satisfactory performance.  This position is eligible for part-time telecommuting contingent on regular video meetings and periodic in-person meetings in Charlottesville.

For any questions on this position please contact Sally Pusede, Assistant Professor, at sep6a at virginia.edu.

For questions on the Resilience Institute, please contact Jonah Fogel at jf3ku at virginia.edu.

For questions about the application process, please contact Rich Haverstrom, Faculty Search Advisor, at rkh6j at virginia.edu.

Additional information about the Institute can be found at https://www.eri.virginia.edu.

For more information on the benefits available to postdoctoral associates at UVA, visit postdoc.virginia.edu and hr.virginia.edu/benefits.

The University of Virginia, including the UVA Health System and the University Physician’s Group 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, marital status, national or ethnic origin, political affiliation, race, religion, sex (including pregnancy), sexual orientation, veteran status, and family medical or genetic information.
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