Location: CSTARS, University of California, Davis, Start Date: available
immediately (negotiable)
Appointment Term: Initially 1 year, renewable for up to 4 more years upon
satisfactory performance and availability of funds
Salary: up to $49,000 a year, depending on qualifications and experience
Benefits: Excellent UC Davis postdoctoral benefit package
U.S. Citizenship: Not required; Foreign candidates may be sponsored for US
non-immigrant visas
Prof. Susan Ustin's Center for Spatial Technologies and Remote Sensing (CSTARS)
at UC Davis is known for more than two decades to be one of strongest
environmental remote sensing groups in the U.S. CSTARS scientists come from
various disciplines including GIS, Computer Science, Applied Math, Geography,
Botany, Ecology, Soil, Atmospheric and Environmental Sciences.
Currently, CSTARS is looking for a Postdoctoral Scholar with excellent
quantitative algorithm development and computer programming skills. The
postdoctoral scientist will collaborate with CSTARS scientists to contribute to:
* enhancement, implementation, and testing of a real-time
remote sensing thermal anomaly detection and classification system prototype:
the GOES Early Fire Detection (GOES-EFD) system;
* developing and testing the conceptual version of the MODIS
Daily Fuel Moisture Content algorithm;
* other projects in the lab.
The ideal candidate will have the following Qualifications and Skills:
A. Ph. D. degree (or near completion) in Remote Sensing/GIS/Computer
Science/Physics/Engineering/Applied Mathematics or a similar field;
B. Excellent problem-solving skills applied to developing image-processing and
pattern-recognition algorithms.
C. Multi-year experience and fluency with computer programming in MATLAB
language (esp. for image/video processing/analysis).
D. Solid theoretical understanding of the following concepts: * Anomaly
Detection, Target Detection, and their underlying mathematical and statistical
principles;
* Machine Learning from examples and Statistical Pattern
Recognition, incl. Feature Selection/Extraction, Supervised/Unsupervised
learning, classifiers and their ensembles;
* Image time series processing and analysis (automatic
image-to-sequence registration, time-series modeling)
* Multivariate Statistical Analysis.
E. Excellent communication skills (written and spoken) in English language, and
good inter-personal skills.
Additional desired qualifications:
I. Working knowledge of IDL/C/C++/GRASS/GDAL
II. Working knowledge of ArcGIS, ENVI,
III. Previous post-doctoral research experience.
IV. Strong peer-reviewed publication record.
V. Vegetation biophysical property retrieval from remote sensing.
VI. Thermal Physics.
VII. Experience with AVIRIS, MODIS, Landsat.
VIII. Background in Remote Sensing (hyperspectral, multitemporal)
Applications should be sent by email and include:
1) Cover Letter which must specifically address the applicant's qualification
with respect to the above requirements A)-F) and I)-VIII).
2) Curriculum Vitae (up to 3 pages) including contact information for at least
two references
3) List of Publications
4) Statement of Research Interests (1-2 pages)
5) Up to three representative publications.
Selected candidates may be contacted to arrange a telephone or an in-person
interview.
Please email your applications materials to:
Dr. Alexander Koltunov
akoltunov at ucdavis.edu
with the subject line: "Postdoc in Remote Sensing: your family name"
