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About the program

"Our Let’s SOLVE it program is part of our wider efforts to encourage and support diverse talent across the AI ecosystems. This initiative provides curious minds and raw talent with the skills, experience and contacts they require to thrive.”

Dr. Eirene Seiradaki

Director, Research Partnerships

Let’s SOLVE it, together

  • If you are an undergraduate student with dreams of pioneering the next game-changing community solution using AI and ML, we want to help you get there.

  • Jumpstart your career

    Build connections with ML industry experts and gain valuable technical guidance and training to explore career or further studies in AI and ML.

  • Solve real problems

    Help improve your local community by creating a viable ML solution that solves a clear community problem. You will get all the support and mentorship from our team to turn your ideas into a viable proof-of-concept.

  • Discover how to apply AI to turn your ideas into reality

    Give this semester more meaning and purpose outside of the course curriculum. We believe students can take ideas and make them happen, using cutting-edge technology and resources we can provide.

  • Work with a diverse cross-functional team

    Get access to a diverse group of industry experts who can help you develop your idea and your skill set.

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  • “Through Let’s SOLVE it, I learned the value of a diverse team. It was so cool to see how our different strengths contributed to the project and how much we learned from each other!”

    Anjola Adewale

    2024 Let’s SOLVE It Alumna; Software and Biomedical Engineering student, McMaster University

  • “Our Let’s Solve it mentor was incredibly helpful throughout the problem-solving process! He treated us like professionals, which motivated us and gave us confidence!”

    Guy Morgenshtern

    2022 Let’s SOLVE It Alumnus; Machine Learning Software Engineer, RBC Borealis

  • “When you are still in school, it’s quite rare to have the chance to collaborate so closely with someone in the AI industry.”

    India Tory

    2024 Let’s SOLVE It Alumna; QTS Software Developer Intern, RBC Capital Markets

  • “We want to support students from a diversity of backgrounds. The Canadian ecosystem is growing and new, diverse perspectives are much needed.”

    Dr. Greg Mori

    Senior Director, Research

  • “It’s great to stay connected with the Let’s SOLVE it students after they return to their studies, bringing along the knowledge they gained during the program!”

    Elahe Rahimian Najafabadi

    Let’s SOLVE it Mentor; Research Engineer, RBC Borealis

How it works

  • We are looking for teams of 3 to 5 undergraduate students with ideas on how AI / ML could be used to tackle a specific community problem. Here’s how the program works:

  • This program is open to all undergraduate students at all Canadian universities.

  • The mentorship program is free and will be conducted in a hybrid mode. With this program, we aim to support students from a diversity of backgrounds, geographic locations and universities.

  • You don’t need to be enrolled in a Computer Sciences program – if team members have some basic programming knowledge, this will help; but specific experience using AI or ML isn’t necessary.

  • The upcoming cycle will run for two months, from September through November 2024. Applications for this cycle are currently open and will close on September 8, 2024.

Frequently asked questions

  • Let’s SOLVE it is a mentorship program aimed at providing undergraduate students with the opportunity to gain industry exposure and networking experience by working closely with members of the RBC Borealis team. During the two months of the program, your teams will work with RBC Borealis mentors on projects using Artificial Intelligence and Machine Learning to tackle the challenges of your community. While working on your selected projects, you will also learn about career opportunities in the thriving AI industry.

    Please gather the following materials in order to proceed with your application. You will not be able to save the application once you start. Select one of your team members to be the team captain.

    Proposal for each team:
    A proposal of 5001000 words. Each team can submit a secondary project; however, please note that the adjudication committee will judge your application based only on your primary project proposal. If your team is selected, both the proposals you submitted will become available to your mentor to choose the best fit for your team to work on during the program.

    Your outline should include the following:
    • Why that problem is important to your team and/or your community;
    • Why your team believes Machine Learning could help solve this problem, and two to three potential datasets to use.
    • If this project is related to a personal, extra-curricular project you have already been working on or if it is part of coursework you have to do for one of your university classes.
    • An example project proposal can be found on Project Proposal PDF.

    Team member info:
    • General information
    • Personal Information
    • Educational background
    • Skills/interests in computer science
    • Proof of enrollment and year of study at a Canadian University for each team member (latest transcripts, letter of acceptance)
    • Resume (optional)

  • This mentorship program is open to teams of 3 to 5 undergraduate students currently enrolled at a Canadian University.

    All team members must meet the following additional criteria:
    • Each team must have 3 to 5 students
    • Enrolled in any program
    • At least one team member must have taken an introductory undergraduate or high school programming course.
    • Each team member must be able to dedicate 10 hours each week to this project between October 1 – November 29, 2024, including one 1-hour team meeting and one 30-minute meeting with their RBC Borealis mentor each week.

  • For any questions not covered by the FAQ section above, please contact mi.research@borealisai.com and use the subject line “Let’s Solve it”.

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The Spring 2025 Cohort