$45,000 per year. The position is for 18 months with the likelihood of additional funding.
We seek an independent and highly motivated remote sensing scientist to develop algorithms for the retrieval of tropical forest bio-physical attributes from Landsat image time series and forest inventory data, and for cloud identification and removal from Landsat imagery. The successful candidate will collaborate with NREL and USFS scientists on the development of algorithms for extracting tropical forest attributes from Landsat image time series and develop software for the efficient implementation of these algorithms. In addition, there will be an opportunity to design and participate in collaborative research that combines products from Landsat image time series with spaceborne lidar or forest inventory data in a range of ecosystem and ecological studies. To be successful, the candidate will lead the development of papers on the algorithms and applications and contribute to related papers.
Minimum Qualifications: PhD in ecology, geography or related field completed by time of appointment, demonstrated experience in applications of remote sensing to forest ecology or forestry, and knowledge of common remote sensing (e.g. Imagine/Envi) and GIS (e.g. ArcGis) packages.
Desired Qualifications: The ideal candidate will have experience in the following areas: experience with phenological approaches to the remote sensing of vegetation, experience with development and application of cloud identification and removal techniques, advanced experience in programming (esp. in Research Systemsí IDL language), experience performing collaborative research with remote sensing scientists on earth system science applications, and experience with tropical forest ecology.
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