Description: We seek a PhD student for a research assistantship in the Landscape Ecology and Fire (LEAF) Lab at Texas Tech University, beginning August 2021. Three years of full support ($25,000/yr stipend, tuition, fees, and fringe benefits) are available through a USDA Hispanic Serving Institution Education Grant, with additional support contingent on funding. The student will help mentor undergraduate cohorts through The Bridge Adventure Program, a program that aims to foster diversity and inclusion through field-based experiential learning opportunities, including regular excursions for mentored research, service learning, and community-building adventure. The PhD student will serve in a leadership role in the program while earning a doctoral degree in Wildlife, Aquatic, and Wildlands Science and Management and conducting dissertation research under the advisement of Dr. Nathan Gill. The student will be at liberty to decide, with advisor input, the nature of research questions for dissertation chapters related to the broader research agenda of the LEAF Lab. Examples of ongoing/upcoming LEAF Lab research themes include:
• Fire regime change in the Southern Rockies
• Invasive plants and fire behavior
• Disturbance, ecoacoustics, and landscape ecology
• Experiential learning and diversity in Natural Resources Management education
To apply: Applicants should submit a resume/cv and 1-page cover letter to firstname.lastname@example.org to describe their interest in the position by December 1, 2020. Please include contact information for at least three references in the cv or cover letter. Applications will be reviewed in the order in which they are received. Questions about the position can be directed to Dr. Nathan Gill at email@example.com or (806) 834-6441. Students from historically underrepresented communities are strongly encouraged to apply.
Qualifications: Applicants with a firm commitment to building a community of diversity and inclusion and Master’s degree (anticipated by August 2021) in Natural Resources Management, Ecology, or other relevant field are encouraged to apply. Experience in programming (R), GIScience, and academic writing is desired but not required. An interest in prescribed fire, fire behavior modeling, National Ecological Observatory Network (NEON) data science, or remote sensing is also desirable.