How the Summer Works
From application to paper submission in 6 months.
Medical Students Submit Projects
If you're a med student with a research idea, submit a proposal describing the clinical problem you want to solve.
Teams Matched
Once matched, you'll meet your team and start planning the project scope and timeline.
ML Fellow Applications
The fellow application window ran from April 27 through May 26. Applications for this cycle are now closed.
Institutional Onboarding
Members of your team will get necessary credentials and access to the tools they need to get started.
10-Week Research Sprint
Build the model, run experiments, analyze results, and write the paper. This is the core of the fellowship.
Present Your Work
Share your findings at the Tensor Lab Symposium and submit your paper for publication.
Common Questions
When does the program start?
The fellowship runs June–August 2026 (10 weeks). We'll announce specific dates in Spring 2026.
Is this remote or in-person?
Mostly remote. You'll meet with your team over Zoom/Discord, but some chapters organize optional in-person sessions.
Do medical students need to know how to code?
No. As a medical student, you bring the research question and clinical expertise. We match you with a ML fellow who handles all the coding.
How much time do medical students spend per week?
About 2-4 hours per week. You'll meet with your fellow regularly to review progress and guide the direction of the research.
Do ML fellows need a medical background?
No. Your medical student partner will teach you everything you need to know about the clinical context. We're looking for strong Python/ML skills.
How much time do ML fellows spend per week?
Plan for 10-15 hours per week during the 10-week summer sprint. Think of it like a serious research internship.
Is this paid?
Not yet. Right now, this is an unpaid fellowship focused on research experience and publication. We're working on funding for future cohorts.
Who owns the code and research?
We believe in open science. Code is open-sourced, and all team members share authorship. Patient data stays under the PI's institutional controls.