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Position TitleEnvironmental Post-Doctoral Statistician
The NREL is a research and teaching unit at Colorado State University with a mission “to ensure the sustainability of the Earth’s ecosystems through the generation and application of new knowledge.” The department’s interdisciplinary, ecosystem research promotes collaborations among disciplines and across fields of expertise at CSU and throughout the world. As a soft-money research unit, this role is critical to ensure adequate funding to sustain its vital role in the research community both locally and across the globe.
Colorado State University is hiring a post-doctoral statistician who will work as part of a team with other statisticians and subject-matter scientists from CSU and US Department of Agriculture. The team is developing and evaluating imputation products combining data from existing USDA surveys and other datasets to represent historical agricultural land use and management in the United States. Dr. Stephen Ogle from the Natural Resource Ecology Laboratory and Dr. F. Jay Breidt from the Department of Statistics will be supervising the post-doctoral statistician in developing the imputation product. The team will conduct assessments of greenhouse gas emissions from agricultural lands using the imputed product with other supporting information. These assessments are used for reporting greenhouse gas emissions from agricultural lands to the United Nations Framework Convention on Climate Change by the United States government. The job will also include preparation of peer-review manuscripts, presentations at professional society meetings and assisting with development of reports describing the methodology and providing emissions data. More information about this position is provided at https://jobs.colostate.edu/postings/75903.
1) Ph.D. in Statistics or quantitative environmental sciences
2) Experience with advanced statistical prediction and/or machine learning techniques
3) Expertise with the statistical package R
1) Demonstrated potential for excellence in statistical modeling
2) Experience with statistical imputation techniques
3) Experience with survey statistics
4) Experience with analysis of agricultural survey data
5) Outstanding verbal and written communication skills