Nexus X Health AI Fellowship
Applications opening soon
A sixteen-week mentored research fellowship. Join a small team and take a research question from concept to a submission-ready paper, learning relevant AI methods along the way — guided by experts in the field.
The focus
Leveraging the Nexus X research pipeline, you will work with an interdisciplinary team spanning clinicians, engineers, data scientists, sociologists, lawyers, and public health researchers, applying AI to tackle the biggest problems in healthcare.
Who it's for
The programme
Weeks 1–2
Land in the state of the art. Journal-club the field, meet the live briefs, and form teams matched by complementary expertise.
Weeks 3–5
Turn ambiguity into a research question: related work, methods, data plan, evaluation criteria, risks.
OutputA written research proposal, defended in a mock review with mentors.
Weeks 6–12
Run the study on real data inside your mentored team. Weekly critique, fortnightly 1:1s, and a midpoint checkpoint against the proposal.
Weeks 13–16
A structured writing sprint, then Demo Day.
OutputA submission-ready short paper or preprint, presented to the cohort, mentors, and invited guests — with a concrete submission plan for a target venue.
Each week: one live methods seminar, one team working session, and protected build time. Fortnightly 1:1 mentorship. Expect around 6–8 hours per week.
Outputs
Mentorship
Mentors are practising researchers who come from the Nexus X lab or from Harvard- and MIT-affiliated labs. Since mentors change from cohort to cohort, we will list the projects and their mentors before applications open. Every fellow has the opportunity to meet 1:1 with their mentor and receive priceless feedback.
Fees
Professional
Clinicians, industry engineers and data scientists, and other salaried professionals.
$3,600USD
Student & Trainee
Enrolled degree students.
$1,800USD
Questions

Nexus X Health AI Fellowship
Small teams, direct mentorship, and a real research output in Health AI. Register your interest and be the first to receive information on the mentor line up, projects, dates, and selection criteria when applications open.
Register interestFull programme details will be shared before applications open.
Prefer self-paced learning?
Guided, self-directed tutorials for learning Health AI at your own pace — no cohort, no application process.
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