Posted: August 8, 2022
Application Deadline: Open Until Filled
A one-year postdoctoral position, with the possibility of extension up to three years depending on funding and performance evaluation, is available to join an interdisciplinary research project, funded by NASA, focused on the harmonization of the MODIS and VIIRS global burned area time series. The objective of the investigation is to generate the first ever global fire record covering the 30 years conventionally required for climate analysis. The position will be based in the Department of Forest, Rangeland and Fire Sciences at the University of Idaho, in Moscow, ID.
The University of Idaho College of Natural Resources is committed to disciplinary and interdisciplinary programs that integrate ecological, social and natural resource science and management systems. Our research, education and outreach sustains people and the land through innovative science, technology and leadership.
Funding This position is contingent upon the continuation of work and/or funding.
A visa sponsorship is available for the position listed in this vacancy. Yes
Key Accountability Assist PI with research by:
Performing intercomparison of coarse resolution MODIS and VIIRS burned area and active fire products
Performing harmonization and calibration of the MODIS and VIIRS fire products.
Analyzing burned area trends and interannual variability
Distributing and documenting research outputs to the scientific community through a dedicated website
Publishing and communicating the project’s findings
Key Accountability Present research results by:
Analyzing and summarizing data in peer reviewed publications
Presenting at scientific meetings
Key Accountability Provide supervision by:
Overseeing work of graduate and undergraduate research students
Programming experience (e.g. C, R, IDL, GDAL)
Ph.D. degree in Engineering, Environmental Sciences, Geography, Natural Resources or equivalent, with a focus on quantitative Remote Sensing.
Required Licensures, Certifications or other
Research experience on the use of NASA MODIS and Landsat Data.
Experience in applied statistics, including sampling theory, validation and calibration techniques.
Demonstrated oral and written communication skills.
Demonstrated record of published peer-reviewed publications. Ability to work independently and collaboratively on scientific manuscripts.
Demonstrated ability to work independently and with a group.
Experience working with the NASA MODIS global fire products, or with other global fire datasets.
Experience working on the fusion of remotely sensed datasets and products Experience working with global environmental satellite products
Familiarity with deep learning algorithms for remotely sensed image classification
Experience in time series analysis
Experience in applied statistics, including sampling theory, validation and calibration techniques