Designing software and hardware for biodiversity monitoring
Alejandra Echeverri, Professor
Environmental Science, Policy and Management
Applications for Spring 2026 are closed for this project.
Traditional biodiversity monitoring tools, such as camera traps and acoustic recorders, are limited in scope, require frequent maintenance, and often fail to capture the full spectrum of wildlife activity. This project aims to transform how we monitor biodiversity by developing solar-powered, AI-assisted monitoring stations that can simultaneously detect birds, mammals, and amphibians. These stations will integrate acoustic, visual, and environmental sensors, and will be capable of transmitting real-time data through cellular or satellite networks.
We envision a future where biodiversity is monitored as routinely as air quality, allowing communities and governments to make informed, inclusive conservation decisions. To achieve this, our team of engineers and wildlife scientists is designing the next generation of biodiversity monitoring hardware and software systems.
Role: The undergraduate intern will contribute to:
1) Prototyping a hardware proof-of-concept for a multimodal biodiversity monitoring node;
2) Implementing or adapting software systems to establish a reliable data pipeline from sensor to cloud;
3) Testing and refining the prototype in a lab setting, preparing it for future field deployment.
There will be opportunities to work with Raspberry Pi hardware, Python for data acquisition and processing, and CAD software for designing custom enclosures and components.
We are looking for a highly motivated student with an interest in interdisciplinary projects at the intersection of mechanical engineering, software systems, and wildlife biology. The ideal candidate is enthusiastic about hands-on prototyping and excited to collaborate with both senior engineers and field ecologists in support of biodiversity conservation.
Qualifications: Preferred Qualifications:
Basic experience with hardware prototyping, such as using Raspberry Pi, Arduino, or similar platforms.
Proficiency in Python, especially for data collection, sensor integration, or automation tasks.
Familiarity with CAD software (e.g., Fusion 360, SolidWorks, or similar) for designing mechanical enclosures or mounting systems.
Interest or coursework in mechanical engineering, electrical engineering, computer science, or environmental science/wildlife biology.
Comfortable working in an interdisciplinary team, and excited to contribute to conservation-driven technology solutions.
Strong problem-solving skills, a hands-on mindset, and a willingness to iterate and learn.
Bonus Skills (not required but a plus):
Experience with remote sensing, image/audio processing, or machine learning.
Familiarity with solar power systems, battery management, or sensor networks.
Prior exposure to field-based environmental research or ecological monitoring.
Day-to-day supervisor for this project: Mel Baldino
Hours: 6-8 hrs
Off-Campus Research Site: This project will mostly happen on campus in Mulford Hall. Once the hardware is developed, we might get to test it in some field sites near the Sacramento-San Joaquin Delta: Twitchell Island Gate: https://maps.app.goo.gl/ceNJgF9ag1F8sM4B6
Related website: https:// https://nature.berkeley.edu/EcheverriGroup/
Engineering, Design & Technologies Environmental Issues