Family Forest Research Center Research Fellow (National Woodland Owner Survey)

The Family Forest Research Center (FFRC) is a collaborative initiative between the University of Massachusetts Amherst and the U.S. Forest Service Northern Research Station. The FFRC is housed in the UMass College of Natural Sciences, Department of Environmental Conservation. Our mission is to deepen the understanding of family forest owners to promote conservation and sustainable forestry. Learn more about us at www.familyforestresearchcenter.org.

The FFRC is seeking a motivated and personable Research Fellow to join our team. This position involves working closely with FFRC directors Dr. Brett Butler and Paul Catanzaro, and other FFRC members, on various projects that explore the attitudes and behaviors of family forest owners and their impacts on the landscape, including the national Woodland Owners Survey (NWOS). Specific responsibilities will be tailored to the fellow’s interests and skills, providing a dynamic and flexible work environment. Projects are to be determined, but will likely include social-justice related topics, including Tribal forest ownership.

Essential Functions

• Collaborate with FFRC team members and external researchers on ongoing research projects.
• Conduct qualitative and quantitative research on family and Tribal forest owners.
• Analyze data and prepare reports, manuscripts, and presentations.
• Contribute to the development of new research initiatives based on the fellow’s interests and expertise.

Minimum Qualifications (Knowledge, Skills, Abilities, Education, Experience, Certifications, Licensure)

• Bachelors degree in a scientific discipline (social science, forestry, environmental science, human dimensions, or related field preferred)
• Two (2) years of related experience.
• Strong analytical skills and experience with qualitative and quantitative research methods.
• Experience writing peer-reviewed journal articles, formal reports, and/or proposals.
• Excellent communication and interpersonal skills.
• Statistical analysis experience preferred (ideally modeling and estimation).
• Proficiency in statistical software (e.g., R) and GIS tools is a plus.