MIT Lincoln Laboratory - Lexington, MA
posted about 2 months ago
MIT Lincoln Laboratory sits at the intersection of academia, industry, and government. Our nationally-recognized staff work collaboratively to solve some of the nation's most challenging problems. As part of the ISR and Tactical Systems Division, we team broadly across MIT Lincoln Laboratory to address a wide variety of problems through analysis, design, prototyping, and testing. Our teaming culture energizes our staff and fosters a high level of collaboration among a wide array of skills and experience levels. In addition, the group values work-life balance, using hybrid arrangements and flexible schedules to maximize productivity. The Embedded and Open Systems Group applies our expertise to developing software and processing architectures for aircraft, ground vehicles, unmanned systems, sensors, radios of all sizes, ground stations, and bases operating across the globe. We develop cutting-edge solutions for the nation's needs in the areas of embedded computing, distributed systems and global battle management for the future. We succeed by participating in all program phases of system analysis, architecture definition, design, prototyping, field testing, and ultimately the transfer of our technology. We get to see our technology make its way from early concepts all the way to flying on test aircraft or fielded at test sites. The group is committed to a diverse and inclusive workplace. We understand that a diverse workplace fosters a creative and productive atmosphere and we are dedicated to making sure that every staff member in the group is supported in their work and that every voice is heard. The Laboratory sponsors resource groups and events dedicated to a wide variety of identities. The Co-Op will join high performing teams to support ongoing programs in the Embedded and Open Systems group to facilitate machine learning at the computational edge in resource constrained embedded systems. Tasks include the labeling, and processing of previously collected data, training, validating and optimizing pre-selected machine learning algorithms to create the appropriate models, implementing the models in an embedded system, supporting lab demonstrations and field experiments, and reporting the results in a scientific paper, among other potential tasks.