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Postdoctoral Scholar: Arizona

Job Description


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Agency
Northern Arizona University
Location
Flagstaff, AZ, USA
Job Category
Post Doctoral Appointments
Salary
$54,779 - $68,474
Last Date to Apply
10/26/2022
Website
https://hr.peoplesoft.nau.edu/psp/ph92prta/EMPLOYEE/HRMS/c/HRS_HRAM.HRS_APP_SCHJOB.GBL?Page=HRS_APP_JBPST&Action=U&FOCUS=Applicant&SiteId=1&JobOpeningId=606602&PostingSeq=1&utm_source=email&utm_medium=email&utm_campaign=nau_tnc_job
Description
The Shenkin Lab at the School of Informatics, Computing, and Cyber Systems (SICCS) at Northern Arizona University’s Flagstaff campus seeks a Postdoctoral Scholar to support a project funded by The Nature Conservancy’s Natural Climate Solutions center. The successful applicant will conduct research to understand how forest management across the Amazon Basin will affect the forest carbon cycle and forest dynamics in future climate change scenarios and will generate recommendations that will serve to conserve those forests. She or he will have the opportunity to develop collaborations with The Nature Conservancy’s and Conservation International’s Natural Climate Solutions teams in addition to the NAU community. The scholar will serve as scientific lead on this project, with primary responsibility for guiding the intellectual development of the methodology for scaling forest management and carbon models from stand to continental scales. Key activities will include: (a) working with PI Shenkin to develop a sound methodology for upscaling carbon models, (b) parameterizing and training remote sensing-based models, (c) developing logging and climate scenarios to across the Amazon Basin, (e) creating Amazon-wide predictive maps of forest carbon trajectories. The bulk of the outputs of the described research will be completed by November 2023. The scholar will work with PI Shenkin and The Nature Conservancy to scope out extensions to this research thereafter. Research and Publications - 85% Work collaboratively with research team to develop sound methodology for upscaling forest management model results to the Amazon Basin. Lead effort to develop and apply remote sensing based models utilizing Machine Learning techniques that form the core of the basis of understanding Amazon-wide forest carbon trajectories. Synthesize climate and land-use scenario research into useful, spatially explicit data layers. Author manuscripts for publication in peer-reviewed journals. Grant and Management - 5% Write brief, regular reports for and participate in meetings with The Nature Conservancy. Policy Outreach - 5% Generate data layers that are the results of the models developed above Coordinate and assist with the integration of those data layers into existing policy outreach tools Other - 5% Other duties as assigned.
Qualifications
Minimum Qualifications Ph.D. in Remote Sensing, Ecological Modeling, Computer Science, Environmental Science, Geography, or a related discipline. Preferred Qualifications Experience using earth observation imagery and climate scenarios Demonstrated ability to conduct spatial analysis using Machine Learning techniques and manage large databases. Experience using a variety of software/geospatial tools. Demonstrated advanced remote sensing and GIS experience. Familiarity with the principles of ecosystem processes and dynamics. Familiarity with lidar data processing and analysis. Interest in working with NGOs to use research to inform policymakers. Ability to communicate effectively in English, and ability to communicate effectively in Portuguese and/or Spanish. Knowledge, Skills, & Abilities Knowledge Knowledge of programming / script writing (Python, R, GDAL, C/C#, SQL, Perl, Java, and/or IDL). Skills Excellent organizational and project management skills. Promotes a diverse, inclusive environment. Abilities Ability to take initiative and complete tasks without direct supervision. Ability to synthesize complex information and develop structured analyses in written and visual form. Ability to perform complex geoprocessing and manipulate large datasets using personally developed and other scripts. Fluency in performing geospatial tasks in a multi-operating system environment. Ability to work independently and as part of an interdisciplinary and culturally diverse research team.
Contact Person
Alexander Shenkin (alexander.shenkin@nau.edu)

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