PhD Graduate Assistantship in Quantitative Fisheries 2026
Organization: University of Florida (Institute of Food and Agricultural Sciences)
Host country: United States
Degree level: Doctoral
Funding: Fully Funded
Deadline: March 15, 2026
Fields of study: Fisheries, Ecology, Natural Resources, Data Science
This PhD position, supervised by Dr. Edward Camp at the University of Florida, focuses on quantitative fisheries research. The project investigates the consequences of changing marine fishery systems on fishers and management, specifically along Florida's Gulf Coast. The position involves quantitative analyses of existing data and socioecological modeling. The successful candidate will join a cohort of students working on related socioecological systems.
Benefits
Annual Salary of $32,000
Full Tuition Waiver (Standard for UF PhD Assistantships)
Research Mentorship and Professional Development
Opportunity to work with federal/state agencies
Eligibility
Must hold an undergraduate degree in fisheries, wildlife ecology, or a quantitative field (MS preferred)
Must have experience with scientific research and quantitative fisheries work
Must have experience using models (e.g., GLMs) in R or similar platforms
Must be admitted to the University of Florida's PhD program
Requirements
Cover Letter (describing research interests)
Current CV
Unofficial Transcripts
Contact information for three references
Required Documents
Cover Letter
CV/Resume
Transcripts
References
Programs of Study
PhD in Fisheries and Aquatic Sciences
Application Process
Email your application materials (Cover Letter, CV, Transcripts, References) directly to Dr. Edward Camp at edvcamp@ufl.edu
Submit a formal application for admission to the UF School of Forest, Fisheries, and Geomatics Sciences
Ensure all materials are sent by the March 15 priority deadline
How to Apply
Send a single email with all required documents to edvcamp@ufl.edu
Subject line should reference 'PhD Assistantship Application'
Also initiate your formal UF Graduate School application
Selection Process
Review of academic background and quantitative skills
Evaluation of research interests and fit with the lab