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CO-OP Student

The Bioengineering Systems and Technologies Group seeks to improve the performance of human-centered missions through preventing injury and disease, improving sensing and identification of people and their environment, and speeding rehabilitation and recovery. This goal is accomplished through four broad technical areas: biomedical research, synthetic biology, bioinformatics and biometrics, and forensics. Biomedical research includes advanced sensing, algorithms, modeling, prototyping, and field testing of technologies to diagnose disease, predict outcomes, avoid injuries, and monitor and enhance human performance. The synthetic biology research area emphasizes the development of tools and techniques that will greatly speed the design, evaluation, and assessment of genome-wide engineering approaches through highly integrated microfluidic devices. Bioinformatics is applied across the group to uncover signatures in high-throughput genomic, transcriptomic, and proteomic data sets. Biometrics and forensics research is developing technologies and systems for human identification, including rapid DNA analysis, standoff biometric sensing, scientific validation of forensic techniques, and integrated architecture analyses. This highly interdisciplinary group draws on skills from biology, biochemistry, biosignal processing, engineering, computer science, physics, and medical research areas. Primary government sponsors are in the Departments of Defense, Homeland Security, and Justice, as well as the National Institutes of Health.

Candidate will participate in projects to develop automated and semi-automated systems for detection and scoring of pathologies in biomedical imagery, including ultrasound, CT, and optical imagery. These projects will be in support of the group's efforts in biomedical research. The candidate will interact with medical experts to gain insight, process large image data bases, develop algorithms to extract relevant features, develop models to classify and predict outcomes, and create data visualizations.


The candidate is a student in a B.S. or M.S. in Bioengineering, Electrical Engineering, Computer Science, or other relevant science or math program. Experience or classwork in a common programming language is required. Proficiency in Matlab and/or Python is strongly desired. Interest in and exposure to machine learning, deep learning, statistical pattern recognition, and/or image processing is a plus.

MIT Lincoln Laboratory
Full time