This collaborative program is designed to create a cohort of students whose individual and small team projects are advised by MIT advisors and industry-professional mentors. Linking the project cohort with industry for additional mentoring and learning opportunities further empowers students’ learning and experience by providing real-world challenges and advice. Mentors have helped to define the projects based on needs in industry or their company. Such exposure can lead to potential recruitment opportunities for the student.
- Students will form small groups and work on projects.
- Projects are motivated by industry problems and interests.
- Projects are advised by MIT advisors and industry-professional mentors.
- The full cohort of students and projects will form a community.
- The cohort will have several common advising, learning and support activities to enhance the group outcomes.
The projects reflect the full breadth of MechE with a particular affinity for the use of simulation, modelling, and data analytics and machine learning in Mechanical Engineering in design, machines, analysis, and systems (e.g. internet of things, additive manufacturing, bio-devices, infrastructure and the environment) and on ways of thinking (critical, creative, analytical, personal, interpersonal, systems, humanistic, computational, etc.).
Please see below a current list of available projects under the MechE Alliance Industry-Connected ELO cohort program along with links to their postings on the MIT UROP Job Board.
For a full description of available projects including relevant figures, please review the IAP/Spring 2021 project concepts document.
- Automating metallographic image analysis
- Coffee roasting
- Condition Monitoring of Offshore Structures
- Deformable materials analysis - dermatology or manufacturing inspection
- Medical data classifiers
- Off-Highway Vehicle Sound Modeling
- Off-Highway Vehicle Tire Modeling
- Radar in the Home
- Tires and multi-materials fabrication - materials analysis
- Tires and tire treads
- Design a very low earth orbit (VLEO) constellation
- Image recognition under water - enhance fisheries management and/or water quality assessments
- Machine learning applied to optical fiber production control systems
- Rotor-flying manipulation simulation - using MATLAB and Simulink
Other Project Topics: IAP students would join current team(s)
Participating Students Will:
For questions related to this program, pleas contact Faculty Lead Dr. Brian Anthony firstname.lastname@example.org or Program Manager Theresa Werth, email@example.com.