Learn Health AI
through research.

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.

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16 weeks
6–8 hours per week: < 10 fellows
Capped by mentor capacity: 1 paper
Submission-ready, your name on it: 1:1 supervision
Fortnightly, plus weekly team critique

The focus

AI research that improves health

Through the Nexus Health research programme, 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.

Framing: Turn a clinical or health-system problem into a research question AI can actually answer, and defend the choice.
Methods & evidence: Design the study, work with real data, and make the analysis stand up to review.
Evaluation: Test assumptions, surface failure modes, and decide honestly what the evidence supports.
Translation: Write and present the work so clinicians, reviewers, and decision-makers can act on it.

Who it's for

Open to diverse backgrounds

Clinicians & health professionals: Turn clinical questions and domain judgment into rigorous AI research, without needing to be an engineer first.
Engineers & Data Scientists: Engineers and data scientists who want their craft tested against consequential clinical problems, alongside people who know the domain.
Humanities, lawyers & social scientists: Interrogate how AI actually impacts health — behaviour, ethics, regulation, equity — by working directly with the people building the systems.
Researchers & students: Graduate students and early-career researchers who want a real project and a submission-ready paper, not another course.

The programme

Stand out after 16 weeks

Weeks 1–2
Orient — the problem space
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
Define — the proposal
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
Build — the execution sprint
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
Demonstrate — the paper
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

What you leave with

A submission-ready paper: Every fellow finishes with a short paper or preprint they can explain and defend — your name, your work.
A Demo Day presentation: Your research, presented to the cohort, the mentor bench, and invited guests.
A shot at publication: The programme drives toward real venues — ML4H, CHIL, NeurIPS and ICML health workshops, JMIR — and mentors guide venue choice and submission. Acceptance is never in our gift, but the work is built to compete.
Co-authorship where it's earned: Fellows whose work contributes materially to Nexus Health research are credited on those publications under normal research norms.

Mentorship

Experts in the field

Mentors are practising researchers who come from the Nexus Health 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

Fees & scholarships

Professional

Clinicians, industry engineers and data scientists, and other salaried professionals.

$3,600USD

Student & Trainee

Enrolled degree students.

$1,800USD

No application fee. Payment only on acceptance.
Employer sponsorship — we invoice your organisation directly and provide a sponsorship letter template.
A small number of need-based scholarships each cohort. Ask in your application; it never affects selection.
Accepted but timing no longer works? Defer your place to the next cohort once, free.

Questions

Frequently asked

How is this different from a course?: Courses are organised around content. The fellowship is organised around contribution: you learn the methods you need while producing real research with mentors and peers.
Do I need to code?: Not necessarily. Teams pair clinical and technical expertise deliberately. Selection weighs what you can contribute, what you want to learn, and how you work with others.
Who owns the work?: Your paper is yours — you keep authorship of your fellowship research. Where work contributes to Nexus Health publications, co-authorship follows normal research norms.
What is the time commitment?: Around 6–8 hours per week for sixteen weeks: one live methods seminar, one team working session, protected build time, and fortnightly 1:1 mentorship.
Can my employer pay?: Yes — we invoice organisations directly and provide a letter template to make the internal case.
Will I be published?: Every fellow finishes with a submission-ready paper and a submission plan; mentors guide venue choice. Acceptance depends on peer review — we build the work to compete, and we don't promise what reviewers decide.

Nexus Health AI Fellowship

Mentored. Selective.

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.

Full programme details will be shared before applications open.

Prefer self-paced learning?

Learn at your own pace.

We're also developing guided, self-directed tutorials for learning Health AI at your own pace — no cohort, no application process.

AI research that improves health

Open to diverse backgrounds

Stand out after 16 weeks

Orient — the problem space

Define — the proposal