Interdisciplinary AI Fellowships 2026: Apply for Up to £1,750,000 (80% of £2,187,500) to Build Domain AI Capability

If you are an established researcher whose work sits outside core artificial intelligence — say climate science, molecular biology, social policy, archaeology, or the humanities — and you want to bring serious AI capability into your field, this is the kind of fellowship that changes how you work. The Turing AI Pioneer Interdisciplinary Fellowships are designed to fund teams led by domain experts who need the resources, expertise, and institutional backing to develop advanced AI approaches targeted at a concrete research challenge in their discipline.

This is not seed money for a vague idea. Think of it as a launchpad: enough budget to hire specialists, set up data pipelines, run large-scale experiments, and create sustained capability that remains with your group after the award ends. But note the gatekeeper: you can only proceed to the full application stage if you first submit an outline and are invited to apply. The outline deadline on the public page is 24 February 2026 (16:00), so plan ahead.

Below I walk through the facts, the strategic playbook, common pitfalls, timelines, and practical next steps so you — and your institution — can give your outline and, if invited, your full application the best possible chance.

At a Glance

Why this fellowship matters (and who will benefit)

Imagine you are a domain expert with a big scientific or social question — for example, predicting soil carbon dynamics at national scale, detecting rare archaeological features in satellite imagery, or modeling treatment response in complex multi-omics patient cohorts. You know the problem and the data. What you might lack is the AI expertise, compute, and team needed to turn domain insight into reliable, reproducible models and capability that your lab or department can sustain.

This fellowship offers that combination: substantial budget, interdisciplinary recognition, and the explicit goal of building domain-relevant AI capability. That means the funders expect not only an exciting technical outcome but also a credible plan for integrating AI into your research environment — training staff, creating data governance, and leaving an enduring capability rather than a one-off model.

Because multiple UKRI councils are involved, panels will value strong domain relevance and evidence that the proposed AI work addresses a genuine bottleneck in that domain. The award is large enough to support dedicated AI hires, significant compute or cloud costs, data curation, and outreach or translation activities such as demonstrators, workshops, or training programs.

What This Opportunity Offers

This fellowship is more than salary money. With an FEC ceiling of £2,187,500 and UKRI funding 80% of that cost, you can craft a program that combines personnel, infrastructure, and community-building work. Expect to budget for the following categories if your bid is competitive: a PI or lead fellowship salary (if applicable within UKRI rules), one or more postdoctoral researchers or research engineers with AI expertise, data engineers, access to high-performance computing or cloud credits, software engineering for production-level tools, travel and workshops to build collaborations, and activities aimed at skills transfer inside your host organization.

Crucially, the funder expects capability-building: not just producing a paper, but embedding AI capacity into your research group or department. That can mean a packaged training program for students and staff, robust data pipelines with documented metadata standards, public code repositories and APIs, and governance structures that cover ethics, data protection, and reproducibility. The most convincing proposals include concrete evidence of institutional buy-in — letters committing lab space, computing resources, and administrative support.

The fellowship supports projects up to three years in duration and requires a start date of 1 October 2026. That gives you a tight window: the outline deadline is 24 February 2026, invitations will follow, and full proposals must be built with the October start in mind. Budget realism matters: this is large funding, but it must map to a clear, phased plan where each pound drives capability and measurable outputs.

Who Should Apply

This opportunity is aimed at established researchers — think senior lecturers, readers, professors, or equivalent — with a strong track record in their domain but whose primary expertise is not “core AI.” That’s the point: domain leaders who want to become AI-capable leaders in their field. Here are some concrete examples of ideal applicants:

• A population health researcher with long-running cohort data who wants to build models to predict treatment trajectories but lacks in-house machine learning engineers and production pipelines.
• An environmental scientist seeking to apply advanced spatio-temporal AI models to satellite and sensor data to create national-scale forecasting tools.
• A historian with massive digitized archives who wants to employ natural language and multimodal models to map cultural networks, but needs help designing annotation pipelines and addressing ethical use.
• A biologist who needs to integrate imaging, sequencing, and clinical records using AI to identify biomarkers and requires data engineering and model validation capacity.

There are also boundary cases to watch: if you already run a lab whose primary focus is AI methods development, this fellowship is not for you. The funders are looking to expand AI capability into domains lacking core AI backgrounds. Early-career researchers (e.g., newly independent PIs) might be eligible, but “established researcher” typically implies a demonstrable record of leadership and management; if you’re unsure, discuss with your research office or the listed contacts.

Insider Tips for a Winning Application

This section is your tactical cheat-sheet. These are practical, specific moves that increase your odds.

1. Tell a domain-first story, then show how AI is the tool. Start by explaining the domain bottleneck in plain terms. Make reviewers who aren’t domain specialists care: what will change in the field if this project succeeds? Then make an explicit argument for why AI is the right method — not because it’s fashionable, but because it solves a tractable and important problem here.

2. Build a real team — not a wish-list. Recruit at least one experienced AI practitioner (research engineer or applied ML lead) who will anchor technical delivery. Pair them with domain postdocs and a data engineer. Include a named collaborator with proven track record for each critical role, and provide CVs that show complementary skills.

3. Show data readiness and governance. You must demonstrate access to the datasets you’ll need, describe data cleaning and labeling plans, estimate compute needs, and explain governance: who owns the data, what approvals are in place, and how you will secure personal or sensitive information.

4. Phase the project and price it carefully. Break the work into phases (proof-of-concept; scale-up; production and handover) and align budget to deliverables. Funders will prefer clear milestones and go/no-go decision points rather than a monolithic “do everything” plan.

5. Prioritize sustainability and training. Explain how you will leave capability behind: staff trained, documented pipelines, an internal training bootcamp, or a new MSc module. Funders dislike one-off prototypes that disappear after the grant ends.

6. Budget the non-glamorous stuff. Allocate money for software engineering, testing, cloud storage, reproducibility audits, and open-source release management. These items are what turn research prototypes into usable tools.

7. Nail the institutional commitment letters. A short, specific letter promising staff time, compute access, and administrative support will carry weight. Generic praise won’t.

If you follow these tips, your outline can act as a compelling invitation magnet — and if you get invited, you’ll already have the skeleton of a full application ready.

Application Timeline (realistic, working backwards)

• 1 October 2026: Project must start. This is fixed; don’t ask for flexibility.
• June–September 2026: If invited and funded, final recruitment and onboarding; set up compute and data access; begin phase 1 tasks.
• April–June 2026: Funding decisions and award negotiation (timing may vary). Prepare for quick contracting and recruitment.
• March–April 2026: If you submitted an outline on 24 February, expect invitation decisions within 4–8 weeks in many competitions. Use this window to assemble draft full application materials so you can move fast upon invitation.
• 24 February 2026 (16:00): Outline application deadline (this is the public deadline for outlines on the official page).
• December 2025–February 2026: Finalize your outline submission. Gather letters of support and a succinct project description that convinces assessors you’re ready to grow AI capability.
• September–November 2025: Begin internal planning: talk to your institution’s research office, identify potential AI collaborators, and map data sources.

This timeline is tight. Start conversations now with potential AI co-investigators and your host institution’s research support office. You’ll want letters and costings ready before an invitation lands.

Required Materials (what you must prepare and how to make each part stronger)

Successful outlines and full applications both demand careful documentation. For the outline, be concise but strategic. If invited, the full application will require a deeper set of materials — prepare these in draft now so you can iterate quickly.

Key documents to prepare (brief list; expand these into full drafts if invited):

• Project summary and case for support: crisp explanation of problem, AI approach, expected outcomes, and legacy.
• Detailed budget and FEC justification: show how the requested 80% maps to salaries, equipment, compute, and overheads.
• Work plan and Gantt chart: phased milestones with measurable deliverables and go/no-go decision points.
• CVs and track record: for PI and key team members, highlighting relevant leadership and technical roles.
• Letters of institutional support: specific commitments for space, compute, staff time, and any matched funding.
• Data management and ethics plans: data flows, FAIR principles, consent and anonymization approaches, and safety/ethical review processes.
• Risk register and mitigation strategies: identify the top technical and operational risks and how you’ll address them.
• Training and legacy plan: how skills and tools will be embedded after the fellowship ends.

Write the project narrative for a mixed audience. Reviewers will include both domain experts and representatives from AI or other councils. Make each section self-contained: if a reader skips to the impact or the budget, they should still see a clear, believable plan.

What Makes an Application Stand Out

Review panels are pragmatic. They fund work that is ambitious but credible. Outstanding applications typically share these features:

• A sharp, domain-centered research question. Funders want to see an articulated problem that matters to the discipline and cannot be solved without the proposed AI capability.
• An integrated team that combines domain leadership with AI engineering expertise. Token inclusion of an AI name is less convincing than a co-lead AI engineer committed to delivering code and pipelines.
• Demonstrated data access and quality. If you need labeled data, show you have it or have a credible plan to create it rapidly (with costed annotation workflows).
• A convincing sustainability plan. Will your department retain trained staff? Will models be maintained? How will outputs be used by practitioners?
• Concrete impact and translation pathways. Whether it’s policy uptake, a public dataset, a deployed service, or a training curriculum, spell out how results will move beyond the lab.
• Attention to ethics, safety, and reproducibility. Clear plans for governance, reproducibility protocols, and ethical oversight elevate credibility.

In short: make it clear that if you get the money, you’ll deliver real capability, not just a few papers.

Common Mistakes to Avoid (with fixes)

1. Treating AI as a black box. Fix: explain model choices in plain terms and show alternatives and fallback plans.
2. Under-budgeting engineering and operations. Fix: allocate realistic staffing and infrastructure costs; consult your IT services for compute quotes.
3. Vague institutional support letters. Fix: ask letter writers to commit to specific resources (e.g., “Provide 1000 GPU hours per month” or “Host two full-time research engineers for 18 months”).
4. Over-ambitious scope in a short time. Fix: phase the work and include measurable milestones with contingency plans.
5. Ignoring data governance and ethics. Fix: prepare consent/approval documents, anonymization strategies, and a clear ethical oversight plan.
6. Leaving training as an afterthought. Fix: detail how you’ll train staff, students, and partners so the capability persists.

Avoid these errors and your application will breathe competence.

Frequently Asked Questions

Q: Do I need to be invited to submit a full application? A: Yes. You must submit an outline application and be invited following assessment to submit a full application. The outline deadline listed is 24 February 2026 (16:00).

Q: Who exactly is eligible? A: Established researchers across UKRI’s remit who do not have a background in core AI research, and who aim to build domain-relevant AI capability. If you’re unsure whether you meet the “established” threshold, consult your institutional research office or the listed contacts.

Q: How much will UKRI fund? A: UKRI will fund 80% of the Full Economic Cost (FEC). The FEC cap is £2,187,500, which makes the maximum UKRI contribution £1,750,000. Funding is subject to final budget approvals.

Q: Can international collaborators be included? A: International collaborators can often be included but cannot normally receive UKRI funding directly. Check specific UKRI rules and discuss with your research office.

Q: Can funds cover salaries and cloud compute? A: Yes — personnel, compute, cloud costs, travel, and other research costs are fundable within the FEC model. Provide detailed justification and consult your institution for overheads and salary banding.

Q: Must the project start on 1 October 2026? A: Yes. The award requires projects to start on 1 October 2026. Plan recruitment and procurement accordingly.

Q: Are early-career researchers eligible to lead? A: The fellowship targets established researchers. Early-career researchers might be included as co-investigators or in key roles, but leadership by a truly early-career PI may not meet the remit.

Q: What happens if my outline is unsuccessful? A: You won’t be invited to submit a full application. Use reviewer or panel feedback (if provided) to improve future bids or plan alternative funding approaches.

Next Steps and How to Apply

Ready to move? Do these six things right away:

1. Read the official outline opportunity page in full: https://www.ukri.org/opportunity/turing-ai-pioneer-interdisciplinary-fellowships-outline-applications/
2. Contact the support team early if you have eligibility questions: [email protected]; [email protected]; [email protected].
3. Convene your core team: domain PI, named AI practitioner(s), data manager, and institutional lead for letters.
4. Draft a tight outline application that emphasizes the domain problem, data access, initial team, and a credible plan to build capability. Keep it concise and compelling — the outline is your invitation ticket.
5. Work with your finance office to build an FEC estimate and note what your institution will provide in kind.
6. Submit the outline by 24 February 2026 (16:00). If invited, be ready to produce a fully costed and detailed proposal quickly with strong institutional letters.

Apply now at the official opportunity page: https://www.ukri.org/opportunity/turing-ai-pioneer-interdisciplinary-fellowships-outline-applications/

If you want help drafting your outline or reviewing a full application once invited, get in touch with your institution’s research support office and consider booking external reviewers who have experience with large UKRI interdisciplinary bids. Good planning now buys you speed later — and for a fellowship of this size, speed and polish often make the difference.