Post-Doctoral Position Available in Unmanned Aerial System (UAS) Remote Sensing of Plant Traits
The Terrestrial Ecosystem Science and Technology (TEST) group (http://www.bnl.gov/TEST/) at Brookhaven National Laboratory is seeking a post-doc interested in developing and using UAS platforms as a basis for monitoring and scaling plant traits. Specifically, this position will focus on building and integrating sensor packages for UAS platforms to develop links between optical, thermal, and structural characteristics of vegetation canopies and biochemical and physiological traits governing carbon, water, and energy fluxes in the terrestrial biosphere. This research will primarily leverage spectroscopic remote sensing observations at the leaf to canopy scales in conjunction with thermal infrared (TIR) sensor data. The successful candidate will work closely with Drs. Serbin and Rogers to: 1) link UAS data with in-situ measurements, 2) use UAS data to measure the drivers of ecosystem function, and 3) provide spatially and temporally resolved trait maps. The essential duties and responsibilities of the post-doc include- Assemble, program and operate UAS platforms Integrate payloads and navigation equipment on UAS platforms Process instrument data, including remote sensing imagery, geolocation and navigation data, and image orthorectification Calibrate and maintain UAS instrumentation payloads Coordinate, measure, and scale key plant processes and traits to link with UAS observations Publish results in peer-review journals and present at scientific conferences Prospective candidates should have- A Ph.D. in remote sensing science, plant biology, ecosystem ecology, ecophysiology, or a related discipline Extensive experience with remote sensing data and its analysis Background in the use of instrumentation for environmental monitoring, such as wireless instrument communication and data retrieval Willingness to work collaboratively in team environments Effective written and oral communication skills Record of publication in high quality internationally recognized journals Preferred Knowledge, Skills, and Abilities- Experience with open-source programming environments such as Python and R, as well as geospatial tools such as GDAL A strong statistical background Experience building and maintaining instrumentation Experience with digital imaging processing and spectroscopy Ability to organize and orchestrate field campaigns Experience using database systems such as PostgreSQL Application Process- Applicants should visit the BNL Careers website (http://www.bnl.gov/HR/careers/ ) and search for Job #180 to apply. Review of applications begins on February 2nd, 2015 and the position will remain open until a suitable candidate is identified. Our preferred start date is April 1st, 2015. Brookhaven National Laboratory (BNL) is an equal opportunity employer committed to ensuring that all qualified applicants receive consideration for employment and will not be discriminated against on the basis of race, color, religion, sex, sexual orientation, national origin, age, disability, or protected veteran status. BNL takes affirmative action in support of its policy and to advance in employment individuals who are minorities, women, protected veterans, and individuals with disabilities.
