Join the Boston Area Research Initiative (BARI) as part of a new project called Common SENSES that is pursuing community-led, science-driven solutions for climate resilience in Boston communities. The project integrates data generated by environmental sensors with participatory workshops with community partners to explore potential green infrastructure implementations. Our current foci are a building being redeveloped in Chelsea, MA’s waterfront and construction being planned for the Blue Hill Ave. corridor in the Roxbury neighborhood of Boston. The student will play a crucial role in the project contributing to some or all of the following: (1) planning and executing participatory workshops; (2) coordinating with multiple community partners; (3) deploying sensors; and (4) modeling sensor data and analyzing the distribution of environmental hazards. Given the interdisciplinary nature of the project, applicants need not be expert in all of these activities. If you are interested in the position but uncertain about fit, please inquire.
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Project Title
Common SENSES
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Faculty / Project Lead
Dan O'Brien
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Project Description
Common SENSES integrates environmental sensors with participatory modeling to facilitate community-led, data-driven solutions for climate resilience in collaboration with Boston communities. The project is led by Michelle Laboy (School of Architecture), Amy Mueller (College of Engineering), Dan O’Brien (School of Public Policy and Urban Affairs; Director, Boston Area Research Initiative), and Moira Zellner (School of Public Policy and Urban Affairs). We are working to build an “intergenerational team,” composed of faculty, staff, a postdoc, and students, with an emphasis on robust collaboration and learning among team members with diverse skills and perspectives. The student will also have the opportunity to integrate with the broader Boston Area Research Initiative team, which is collectively working on a range of projects.
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Qualifications Necessary
Applicants should be passionate about community-engaged scholarship and have comfort communicating with partners outside academia. Those hoping to contribute to data analysis should have experience working with R programming language. Experience with GIS software and Python also a plus.