USFS Streamflow Permanence Research Opportunity

USFS Office/Lab and Location: This fellowship is available with the US Department of Agriculture (USDA) Forest Service's (USFS) located in Corvallis, Oregon.

At the heart of the U.S. Forest Service's mission is their purpose. Everything they do is intended to help sustain forests and grasslands for present and future generations. Why? Because their stewardship work supports nature in sustaining life. This is the purpose that drives the agency’s mission and motivates their work across the agency. It’s been there from the agency’s very beginning, and it still drives them. To advance the mission and serve their purpose, 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.

Research Project: The fellow and trainee in these appointments will be involved in our team's flow permanence project. Their learning will involve the full cycle of research: starting with navigating in the field to collect observations using the FLOw PERmanence (FLOwPER) app, then conducting research on quality control (QC) for archiving of those data and input of data to analytical models. QC includes anomaly detections, geospatial processing and analyses using ArcGIS pro. Fellows will have the opportunity to learn R and Python software for data engineering as they explore novel variable combinations for characterization of conditions pertinent to streamflow. Fellows will conduct research using the Western Oregon Wet Dry model to generate predictions of flow permanence based on landscape, geology, vegetation and other factors. 

Learning Objectives: Over the course of the fellowship fellows will gain experience with documenting processes and data with metadata, sharing R and Python code using GIT, and documenting code with Jupyter Notebooks. They will be involved in presentations of findings to at scientific meetings and meetings with managers. These activities will enhance their professional development, build confidence and analytical skills, familiarize them with USDA research topics and expose them to future career opportunities.

Mentor: The mentor for this opportunity is Jonathan Burnett (jonathan.burnett@usda.gov). If you have questions about the nature of the research, please contact the mentor.

Anticipated Appointment Start Date: June 2023.  Start date is flexible and will depend on a variety of factors.

Appointment Length: The appointment will initially be for three months but may be extended upon recommendation of USFS and is contingent on the availability of funds. 

Level of Participation: The appointment is full-time.

Participant Stipend: The participant will receive a monthly stipend commensurate with educational level and experience. 

Citizenship Requirements: This opportunity is available to U.S. citizens only.

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 USForestService@orise.orau.gov and include the reference code for this opportunity.

The qualified candidate should have received a bachelor's, master's, or doctoral degree in one of the relevant fields, or be currently pursuing the degree with completion before October 1, 2026. Degree must have been received within the past three years.

Preferred Skills: