Postdoctoral Scholar with Wildfire Resilience Initiative in Dept of Geography
2 weeks ago
Position description
The Department of Geography, at the University of California, Santa Barbara seeks a qualified candidate for a post-doctoral research position with a primary focus on wildfire fuels, fire behavior and management within the Wildland Urban Interface (WUI). Qualified applicants must have an interest in wildfire related research, preferably with training in remote sensing, geographic information science, spatial statistical modeling, and fire ecology. The qualified candidate will work with a multidisciplinary team that is integrating remote sensing, GIS, laboratory studies of plant flammability properties, climate patterns, fire weather and the social, economic and demographic aspects of risk to improve community resilience in the presence of wildfire. The qualified candidate will concentrate on improved measures of wildfire fuels, and hazard in the WUI, but is also expected to contribute to integration of broader team efforts. The candidate will work closely with UCSB PIs Dr. Dar Roberts and Dr. Max Moritz, working with spaceborne, airborne and drone-based imagery, plant water balance metrics, and other relevant geospatial data layers, primarily focusing on Santa Barbara County. The candidate is expected to coordinate research activities, develop refereed publications, engage in local and state outreach activities, and present research results at professional conferences.
The University is especially interested in candidates who can contribute to the diversity and excellence of the academic community through research, teaching, and service as appropriate to the position.
Department :
Qualifications
Basic qualifications (required at time of application)Applicants must have completed all requirements for a PhD Degree in Geography/Remote Sensing, GIScience, Biogeography, Plant Ecology, Wildfire Science or a related discipline except the dissertation at the time of application.
Additional qualifications (required at time of start)PhD awarded by the time of appointment.
Preferred qualifications Strong expertise in handling, processing and visualizing large spatial vector and gridded datasets ( remotely sensed and climate/ecohydrologic forecasts)Strong background in applied statistical modeling and machine learning Experience with processing and analyzing climate and/or plant water balance forecasts is strongly preferred. Ability to implement predictive models (including but not limited to machine learning algorithms) based on gridded datasets ( remotely sensed or climate/ecohydrologic forecasts, time series, and/or panel datasets). Fluency in programming languages such as R, Python or IDL. Expertise in the use of standard image processing software such as ENVI, QGIS of Google Earth Engine Demonstrated ability to make reproducible code for cleaning, integrating, and modeling spatial/temporal data from multiple sources, spatial scales, and temporal frequencies. Interest and experience in applied sciences Proven record of independently leading research projects, conducting reproducible research, publishing journal articles and presenting at international and national conferences.
Application Requirements
Document requirementsCurriculum Vitae - Your most recently updated
Cover Letter
Reference requirements 3 required (contact information only)References will be contacted for final candidate(s) by email and/or phone, requesting one (or more) of the following: discussion over email or phone, letter of recommendation.
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