USFS Postdoctoral Fellowship in Economics of Climate Change and Water: Colorado

Updated: 26 days ago
Location: Fort Collins, COLORADO
Deadline: 15 Dec 2021

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U.S. Department of Agriculture (USDA)
Fort Collins, Colorado
Job Category
Post Doctoral Appointments
$63,000/yr. stipend
Last Date to Apply
*Applications will be reviewed on a rolling-basis. USFS Office/Lab and Location: Multiple research opportunities are available with the U.S. Forest Service (USFS) Rocky Mountain (RM) Research Station located in Fort Collins, Colorado. The U.S. Forest Service balances the short and long-term needs of people and nature by: working in collaboration with communities and our partners; providing access to resources and experiences that promote economic, ecological, and social vitality; connecting people to the land and one another; and delivering world-class science, technology and land management. The Rocky Mountain Research Station develops and delivers scientific knowledge and innovative technologies with a focus on informing policy and land-management decisions. Our researchers work in collaboration with a range of partners, including other agencies, academia, nonprofit groups, and industry. The Rocky Mountain Research Station serves the Forest Service as well as other federal and state agencies, international organizations, Tribes, academia, non-profit groups and the public. Research Project: The research group studies water resources, particularly threats to those resources due to climate change, land use, sea level rise, and wildfire. The goal of the project is to assess trends in water resources, project future supply and demand of water across uses, and analyze potential solutions to reduce water stress. The group includes hydrologists and data scientists, and a key part of the position is synthesizing insights across disciplines. Group research is centered around the water assessment for the Resource Planning Act ( and contributes to the National Climate Assessment. The research program is highly computational involving large datasets. Most of the work is done in R, though languages such as Matlab, Python, and GAMS are used as needed. Experience with general equilibrium models or machine learning is useful. Participants will both contribute to and lead peer-reviewed publications. Learning Objectives: Participants will learn details of climate and water modeling, with a focus on scenarios used in the IPCC Assessment Reports. Participants will gain an understanding of how climate scenarios impact natural resource use and management. Mentor: The mentor for this opportunity is Travis Warziniack ( If you have questions about the nature of the research please contact the mentor. Anticipated Appointment Start Date: Spring or Summer 2022 (multiple openings available). Start date is flexible and will depend on a variety of factors. Appointment Length: Ideally, the research fellow would spend 2-3 years on appointment, conditional on a successful first year and funding. Level of Participation: The appointment is full-time. Participant Stipend: $63,000 annually Citizenship Requirements: This opportunity is available to U.S. citizens, Lawful Permanent Residents (LPR), and foreign nationals. Non-U.S. citizen applicants should refer to the Guidelines for Non-U.S. Citizens Details page of the program website for information about the valid immigration statuses that are acceptable for program participation. ORISE Information: This program, administered by ORAU through its contract with the U.S. Department of Energy (DOE) to manage the Oak Ridge Institute for Science and Education (ORISE), was established through an interagency agreement between DOE and USFS. Participants do not become employees of USDA, USFS, DOE or the program administrator, and there are no employment-related benefits. Proof of health insurance is required for participation in this program. Health insurance can be obtained through ORISE. Questions: Please visit our Program Website. After reading, if you have additional questions about the application process please email and include the reference code for this opportunity.
The qualified candidate should be currently pursuing or have received a doctoral degree in one of the relevant fields. Preferred skills: - Strong modeling skills - Proficiency in R - Experience working with large datasets - Some willingness to learn languages such as Matlab, Python, and GAMS -Experience with either general equilibrium modeling or machine learning
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