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Quantitative Ecological Post-Doc: Minnesota
University of Minnesota in Saint Paul, Minnesota
 
 
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Date Posted 09/19/2021
Category
Admin-Other Administrative Positions
Employment Type
Application Deadline Open until filled
 
 
 
 
 

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Agency
University of Minnesota
Location
St. Paul, MN or remote work potential
Job Category
Post Doctoral Appointments
Salary
$50,000 but commensurate with experience
Start Date
10/01/2021
Last Date to Apply
10/01/2021
Website
https://silvalab.cfans.umn.edu/
Description
The SilvaLab at the University of Minnesota Department of Forest Resources is seeking a Post-Doctoral Associate to support the data analysis for several ongoing research projects which include floodplain forestry dynamics (growth and yield including stand density tables, analysis of structural and compositional complexity at multiple scales) and individual species and forest community response to biochar (individual tree measurements, time series analysis, plant community measures of plant diversity, and below ground metrics related to soils). The individual will work in collaboration with scientists and researchers from the University of Minnesota and the USDA Forest Service. The position consists of (1) processing and analysis of different data sets of the projects described above (40%), establishment of data management, assessment, and quality protocols, (20%), and development of peer reviewed publications (40%). The individual will use various analytical and assessment approaches to quantify metrics of stand structure, composition, and diversity across multiple spatial and temporal scales to develop increased basic understanding of forest ecosystems and applied knowledge related to forest management.
Qualifications
Required Qualifications: PhD in quantitative silviculture, statistics, biometrics, applied forest ecology, or a closely related field. A strong work ethic, the ability to work independently and cooperatively with researchers and analysts, and demonstrated analytical skills and writing capability are required. Preferred Qualifications: Experience working with multiple types of data including high-frequency data, spatially mapped data, long-term datasets; experience working with forest managers; proficiency in statistical models and programs (R, SAS, etc.), publishing in peer-reviewed journals.
Contact Person
Marcella Windmuller-Campione
Contact eMail
mwind@umn.edu

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