San Diego Zoo Wildlife Alliance offers undergraduate Summer Fellowships on our Conservation Science teams. The Conservation Technology Laboratory within the Population Sustainability department is seeking two fellows for summer 2024.
The Conservation Science Summer Fellowship is a 12-week program where undergrads work directly with a mentor from the Conservation Science team of their choice. Within those 12 weeks, the fellows gain hands-on experience while completing a project. Fellowships are based at the Beckman Center (Escondido, CA) unless otherwise stated.
Applications for the 2024 Conservation Science Summer Fellowships can be submitted until February 15, 2024. Applicants should specify which team’s fellowship they are applying for; applicants can only apply for one team, not multiple teams.
Current undergraduate students must be officially associated with a college or university to be eligible for the fellowship program. Undergraduates who are currently enrolled in college meet this criterion, as do graduating seniors who are continuing their education in the fall following graduation. Recent graduates who are not continuing their education in the fall of the same year are not eligible to apply.
Summer 2024 Conservation Science Summer Fellowships
- May cohort: May 20–August 9
- June cohort: June 24–September 13
Fellowships are 40 hours per week for 12 weeks. Fellows will receive a $7,000 stipend. To apply, submit your resume and cover letter to Holly Davis at [email protected]
- One fellow with interest and capabilities in computer vision and/or machine learning. The fellow will work on software systems for processing image and/or video data from field camera systems deployed in a variety of ecosystems globally to automatically produce derived data about the species and individuals present in the imagery and/or their behavior.
- One fellow with an interest and capabilities in programming embedded systems, particularly in contexts where power is limited. Interest in machine learning inference on constrained devices (e.g. TinyML) and/or low-power radio communication (e.g. LoRa) is a plus. The fellow will work on projects where image and/or inertial data is processed directly on field equipment so that small reports can be transmitted over low-bandwidth connections and/or the equipment can respond autonomously.