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