Ph.D. positions in boreal forest ecology at the University of Virginia

4 days left

Charlottesville, Virginia
UVa provides a competitive package for graduate students, including stipends, health insurance, and
Aug 24, 2020
Sep 23, 2020
Career Level
Student / Graduate
Education Level
Job Type
Relocation Cost
No Relocation
Sector Type

The Department of Environmental Sciences at the University of Virginia is seeking 1-2 Ph.D. students to work with Xi Yang ( and Howie Epstein ( on an NSF-funded project that studies the function and structure of various vegetation communities within boreal forests, and their role in the carbon cycle using field observations and remote sensing. We use a wide range of remote sensing tools including imaging spectroscopy, solar-induced chlorophyll fluorescence, thermal remote sensing, LiDAR, and Unmanned Aerial Vehicles. Students are expected to conduct fieldwork in Alaska. Highly-motivated students with a background in one or more of the following areas are encouraged to apply: remote sensing, ecosystem ecology, or ecophysiology. Strong programming and quantitative skills are also beneficial for the applicant. Interested students should send a CV, informal transcript, and TOEFL scores (if applicable), and a brief statement of research interests to Dr. Xi Yang ( and Dr. Howie Epstein (

Please email with the title “UVa Ph.D. Program [Your name]”. We are looking for one to two students, and at least one student to start the spring of 2020 (early to mid-January).

Information about the Department of Environmental Sciences can be found at:

The University of Virginia, including the UVA Health System and the University Physician’s Group are fundamentally committed to the diversity of our personnel.  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 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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