Casual Research Specialist

University of Minnesota, United States

Updated: 14 days ago
Location: Minneapolis, MINNESOTA

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Casual Research Specialist
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Job ID
372765
Location
Twin Cities
Job Family
Supplemental Employee
Full/Part Time
Part-Time
Regular/Temporary
Regular
Job Code
0001
Employee Class
Civil Service
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About the Job
 

The Shaw Lab at the University of Minnesota, Twin Cities is seeking a temporary research specialist for a funded project on evolutionary responses to climate change and consequences for geographic range dynamics. Our project specifically focuses on improving forecasts of range expansion for plant species by incorporating knowledge of local adaptation to historical climate variation and its implications for responses to contemporary climate change. The project includes large-scale precipitation and temperature manipulation in combination with reciprocal transplants of genotypes of common tansy (Tanacetum vulgare) and paper birch (Betula papyrifera) across four regions of Minnesota. Part of this experiment has already been established and will continue for at least two additional years. The postdoc will lead data collection (for paper birch), statistical analysis, and publication of major results from the experiment. They will also lead the development of predictive distribution models that incorporate data from the experiment. The project is funded by the USGS CASC.
 


Qualifications
 

Required Qualifications:

  • A completed Ph.D. in ecology, evolutionary biology, statistics or a related field. 
  • Experience with field work and field experiments with plants
  • Experience with statistical modeling and basic statistical concepts
  • Ability to travel to research sites in Minnesota with intermittent overnight stays at field stations or similar accommodations
  • Excellent English writing and oral communication
  • Strong organizational skills
  • Ability to work in a collaborative environment


Preferred Qualifications:

  • Promising record of research productivity
  • Track record of publication in strong journals
  • Familiarity with distribution modeling

 


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