At SERC, the Technology in Ecology Lab collaborates extensively to design and support technological innovation and production to serve ecological research, experimental design and data infrastructure.  We develop and support several ongoing climate change experiments such as SMARTX, GENX, and MERIT, and maintain sensor and data infrastructure for the MarineGEO chemical and physical observations program. Additionally, we have been developing open source, “D-I-Y” solutions for ecological research. Ongoing work includes Arduino and Xbee-based dataloggers and feedback controllers for ecosystem manipulation experiments and developing methods for DIY measurements of CO2 and methane production in coastal wetland systems. We operate at the interface of ecology, engineering, and programming and leverage work from experiments to build lower cost sensor systems.

We are seeking an intern to advance our Breathe Baltimore Community-Based Air Quality Monitoring Project. Co-led by the EJJI, this initiative aims to advance low-cost air quality monitoring to support communities in South Baltimore affected by poor air quality. Traditional air quality monitoring methods often lack the spatial and temporal resolution needed to capture hyperlocal variations, leading to mischaracterization of regional air quality. By developing affordable sensor-based solutions, our project seeks to increase the accessibility of real-time air quality data and empower communities.

We are seeking a curious and proactive individual who thrives in an adaptive learning environment and can work both independently and collaboratively. The ideal candidate should be eager to troubleshoot challenges, confidently seek guidance when needed, and contribute their own ideas to improve system design and implementation. This internship will offer hands-on experience working with emerging environmental sensor technology, electronic prototyping, and data processing. Work will be conducted in both lab and field settings, with occasional outdoor testing to evaluate sensor performance in real-world conditions.