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Postdoctoral Associate

University of Texas at El Paso
Cambridge, Massachusetts, US
Closing date
Nov 9, 2021

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Academic / Research
Conservation science
Remote Sensing
Salary Type
Employment Type
Full time
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Job Description Working at MIT offers opportunities, an environment, a culture - and benefits - that just aren't found together anywhere else. If you're curious, motivated, want to be part of a unique community, and help shape the future - then take a look at this opportunity.

POSTDOCTORAL ASSOCIATE, Civil and Environmental Engineering (CEE), to work in the area of environmental remote sensing as part of a collaborative, NSF-funded project. Will focus on detecting and estimating fine-grained changes in tropical forests mediated by both natural and man-made forces. Will join a collaboration between Saurabh Amin, Dave Des Marais, Dara Entekhabi, and Charles Harvey and be directly advised by one or two of the principal investigators according to the postdoc's interests/background. The broader project addresses illicit supply networks, particularly the timber trade, with a focus on using analytics and model-based tools to detect and disrupt illegal operations in these networks. Will also have an opportunity to develop an independent research project related to the central aims of the program.

Additional details about the project are available at

CEE comprises diverse research areas spanning sciences and engineering, with particular strengths in environmental life science, network science, systems engineering, and remote sensing. It is a community united in its pursuit of intellectual, creative, and technical excellence to make a better world. CEE cares about the mental and physical health of its students, faculty, and staff as a human priority before the nature or progress of their work. Diversity is considered critical for the department to achieve its mission and it values diversity in and inclusion of its students, postdocs, faculty, and staff and appreciates their backgrounds and opinions.

Job Requirements
REQUIRED: Ph.D. in ecology, forestry, environmental science, or environmental engineering. PREFERRED: strong preparation in remote sensing or ecosystem dynamics. Those with a background and interest in using statistical inference and machine learning tools are encouraged to apply. Job #19726

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