Who is TRiCAM for?
We seek undergraduates from all STEM disciplines who are currently enrolled in any degree granting institution. Students without prior research experience are especially encouraged to apply.
What will I learn?
TRiCAM's team-based approach to research teaches participants an appreciation for scientific collaboration as well as responsible research conduct. It provides opportunities to practice conflict resolution skills in a controlled environment and exposes students to careers in computational and applied mathematics.
Students learn to use computational and mathematical tools relating to data analysis, simulation, and machine learning. They are trained to independently seek solutions to technical problems as they work to solve a challenging research problem.
The ten week program includes seminars, training, problem solving, social events, and professional development.
What kind of project will I work on?
Projects involve the application of computational and mathematical tools to solve problems in fields including geoscience, medicine, materials science and the social sciences. Projects are chosen to appeal to a wide population of students who are at early stages of their academic development, and who have limited awareness of the vast range of potential career paths in applied mathematics and computational science. See the projects page for more information.
How are participants chosen?
Participants will be selected for teams based on their individual academic strengths and for their potential fit as a member of a team and for a particular project.
Will I be paid?
The TRiCAM program provides a $5000 stipend along with $350 in travel funds for students to spend the summer at Harvard University in the lovely Boston area. Students live on campus during the program and housing is provided.
Comments from past participants
“I developed leadership skills from working so closely with my team. "
- “My project was engaging and I was able to explore cool things such as facial recognition and expression analysis. I could see myself doing something like this in the future for a job.”
- "We all had something to offer. I learned a lot from my teammates as well as the other teams.”