*Applications will be reviewed on a rolling-basis.

USDA Forest Service Office/Lab and Location: A fellowship opportunity is available with the US Department of Agriculture (USDA) Forest Service (FS) within the Forest Service Southern Research Station (SRS). The opportunity is remote.

The USDA Forest Service Forest Inventory and Analysis (FIA) is the primary source of authoritative information on status, condition, and trends of U.S. forests and the products and services they provide. FIA’s Resource Use Monitoring component consist of two data collection efforts- the Timber Products Output (TPO) and the Harvest Utilization (HU) studies. Through HU, FIA generates estimates of harvested volume left on site (logging residue). Both data collections help provide key timber production estimates to inform a variety of analyses including wood basket, wood markets, sustainability, wood supply and demand, etc. This information is used by customers in both the private and public sectors, including State agencies, forest industry, the Federal Government, and others. For additional program information visit https://research.fs.usda.gov/programs/fia

Research Project: Tree volume equations used in the estimation of tree harvest utilization volume estimates have been recently updated, prompting a need for an impact evaluation. Under the guidance of a mentor, the participant will have the opportunity to be involved in the management and analysis of harvest utilization (HU) data collected by FIA. The participant will collaborate with FIA staff to enhance HU data management and applications.

Learning Objectives: Under the guidance of a mentor, learning activities include:

• Exploring HU sample methodologies and potential association to FIA plot data.
• Identifying and documenting methodology to transfer HU data from western records to the national FIA database,
• Analyzing the effects of the new volume equations for HU estimates,
• Generating data visualization tools for HU data.
• Analyzing trends in US HU levels.

Mentor: The mentor for this opportunity is Consuelo Brandeis (consuelo.brandeis@usda.gov). If you have questions about the nature of the research, please contact the mentor.

Anticipated Appointment Start Date: January 6, 2025.  Start date is flexible and will depend on a variety of factors.

Appointment Length: The appointment will initially be for one year but may be extended upon recommendation of USDA Forest Service and is contingent on the availability of funds.

Level of Participation: The appointment is full time or part time, full time preferred.

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, 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 USDA Forest Service. Participants do not become employees of USDA, USDA Forest Service, 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 ORISE.USFS.SRS@orau.org and include the reference code for this opportunity.

Qualifications

The qualified candidate should be currently pursuing or have received a bachelor's, master's, or doctoral degree in the one of the relevant fields.

Preferred skills include:

• Knowledge with SQL strongly desired
• Demonstrated statistical skills including analysis and data management of quantitative datasets
• Experience using statistical software, including Stata and R, and using visualization tools (e.g., Excel, Tableau)
• Strong organizational skill and demonstrated ability to perform both independently and as part of a team