OCRA Global Ovarian Cancer Research Consortium AI Accelerator Grant 2026–2027: A $1 Million, Three-Year Award With AWS Cloud Support for Data-Intensive, AI-Driven Ovarian Cancer Research

OCRA Global Ovarian Cancer Research Consortium AI Accelerator Grant 2026–2027: A $1 Million, Three-Year Award With AWS Cloud Support for Data-Intensive, AI-Driven Ovarian Cancer Research

Ovarian cancer remains one of the most lethal gynecologic cancers, in large part because it is usually caught late and because the disease is biologically diverse. The AI Accelerator Grant, run by the Global Ovarian Cancer Research Consortium and administered by the Ovarian Cancer Research Alliance (OCRA), was created to change that trajectory by pointing modern data science and artificial intelligence at the hardest questions in the field. It is one of the larger single awards available to ovarian cancer researchers: up to $1 million per grant over three years, paired with in-kind cloud computing support from Amazon Web Services (AWS).

This guide explains what the grant funds, who it is for, how the annual cycle works, and how to build a competitive application. It is written for principal investigators, computational biologists, data scientists, and clinician-researchers who want to understand whether this program fits their work before they invest weeks in a proposal.

Key Details at a Glance

Note on timing: the LOI for the 2026 cycle closed on June 23, 2026, and invited full proposals are due in September 2026. As of this writing the specific dates for the next cycle have not been published. Because the program has run on an annual rhythm anchored to World Ovarian Cancer Day, teams planning for 2027 should treat the dates above as the pattern to monitor, and confirm the exact deadlines on OCRA’s grant pages before building an application.

What the Grant Offers

The headline is money plus infrastructure. Each AI Accelerator Grant provides up to $1 million in direct funding over a three-year term. That scale is meant to support genuinely data-intensive work — assembling and harmonizing large datasets, training and validating models, and doing the careful clinical and biological interpretation that turns a model output into a usable insight.

Just as important is the in-kind component. The program pairs its financial award with cloud computing support from AWS, so that teams are not blocked by the cost of large-scale compute. For groups that have the scientific idea and the clinical data but lack the budget for sustained GPU time and storage, that combination is the difference between a pilot and a full study.

The grant grew out of a consortium model. It was introduced in 2025 in partnership with Microsoft’s AI for Good Lab, and the consortium has since brought together four national ovarian cancer organizations: the Ovarian Cancer Research Alliance (United States), Ovarian Cancer Action (United Kingdom), Ovarian Cancer Canada (Canada), and The Ovarian Cancer Research Foundation (Australia). The aim of pooling expertise and resources across countries is to move faster than any single national program could on its own.

Research Priorities: Where Funded Projects Focus

The consortium has been explicit about the problems it most wants to solve. Competitive proposals should map clearly onto one or more of three priority areas:

• Prevention, initiation, and early detection. Ovarian cancer’s poor survival is tied to late diagnosis. Work that improves risk stratification, finds earlier biomarkers, or models the earliest steps of disease is squarely on target.
• The tumor ecosystem. This covers the tumor microenvironment, immune interactions, spatial biology, and the complex signaling that shapes how ovarian cancers grow and spread.
• Treatment resistance. Many patients respond initially and then relapse. Projects that predict, explain, or overcome resistance to chemotherapy, targeted agents, or immunotherapy address one of the field’s central frustrations.

Across all three, the common thread is that AI or advanced data science must be central, not decorative. The program is designed to support projects that use modern computational approaches to extract signal from complex, high-dimensional data — genomics, imaging, digital pathology, electronic health records, or multi-omic datasets. A proposal that mentions machine learning only in passing is unlikely to compete against teams for whom the computational method is the engine of the science.

Who Should Apply

Funding is available to researchers based at accredited non-profit universities and colleges, non-profit research hospitals, and recognized non-profit scientific research facilities. Commercial entities and individuals outside such institutions are not the intended recipients.

The program is international in outlook. Principal investigators working at non-US institutions are eligible, and recent cohorts have been notably cross-border: OCRA has described a field in which international teams, each comprising researchers from Australia, Canada, the United Kingdom, and the United States, competed for the award. That signals a strong preference for collaboration that pools data and expertise across the consortium’s countries. If you are assembling a team, think early about whether adding co-investigators in other consortium nations strengthens your data access, your methods, or your path to validation.

The best-fit applicant is a team that combines three things: deep ovarian or gynecologic cancer domain knowledge, real access to meaningful data, and genuine AI/data-science capability. Teams that are strong on only one axis — for example, excellent modelers with no clinical grounding, or clinicians with a dataset but no computational partner — should consider recruiting collaborators before applying.

How the Application Works

The AI Accelerator Grant uses a two-stage process, and all submissions go through SmartSimple, OCRA’s online grant management system.

1. Letter of Intent (LOI). Every team begins with an LOI that outlines the proposed research: the problem, the data, the computational approach, the team, and the expected impact. This stage is open to all eligible applicants.
2. Full Proposal (by invitation). Only teams whose LOIs are selected are invited to submit a full proposal. The full proposal is where you provide the detailed scientific plan, methods, milestones, data-management approach, and budget.

For the 2026 cycle, the LOI opened on World Ovarian Cancer Day (May 8, 2026), the LOI was due Tuesday, June 23, 2026, at 5:00 PM ET, invited full proposals were due Thursday, September 10, 2026, at 5:00 PM ET, and funding notifications were expected in mid-November 2026. Use that sequence as your planning template for the next round, and verify each date against the official pages once the new cycle opens.

Questions about eligibility, the platform, or the process can go to [email protected].

Timeline and Planning

Because this is an annual program with an LOI stage well ahead of the full proposal, the practical planning horizon is longer than the deadlines suggest. A sensible rhythm looks like this:

• Two to three months before the LOI opens: confirm your team, clarify data access and any data-sharing agreements, and settle on the core computational idea.
• World Ovarian Cancer Day (early May), when the LOI typically opens: draft and submit a tight, specific LOI that makes the AI contribution and clinical relevance obvious.
• Summer, if invited: build the full proposal, including a realistic three-year budget, a data-management and compute plan that takes advantage of the AWS support, and clear milestones.
• Autumn: submit the full proposal by the September deadline and await decisions, which have historically arrived by mid-November.

Building data agreements and cross-institution collaborations is usually the slowest part, so start those conversations before the portal opens rather than after.

Required Materials and What Reviewers Look For

Exact requirements are specified in the RFP for each cycle, and you should read the current version in full. In general, expect the LOI to ask for a concise description of the scientific problem, the data, the AI or data-science method, the team, and the anticipated impact. The invited full proposal will ask for a detailed research plan, methods, timeline and milestones, a data-management plan, a budget and justification, and investigator credentials.

Reviewers on a program like this are weighing several things at once:

• Scientific importance: does the project address a real bottleneck in one of the priority areas?
• Computational rigor: is the AI method appropriate, well-justified, and validated — not just fashionable?
• Data quality and access: do you actually have, or have a credible plan to obtain, the data your model needs?
• Feasibility: can this team deliver in three years with the requested budget and the provided compute?
• Translational path: how would a positive result change screening, biology, or treatment?

Strong applications make the AI contribution concrete: what the model ingests, what it predicts or discovers, how performance is measured, and how the result is validated against biology or outcomes.

Common Mistakes to Avoid

• Treating AI as a buzzword. If the computational method could be removed without changing the project, the proposal is not a fit for this grant.
• Underestimating data logistics. Access agreements, harmonization, privacy, and governance take time; unresolved data plans undermine feasibility.
• Ignoring validation. A model with impressive internal metrics but no external or biological validation is a red flag for reviewers.
• Weak translational framing. Show how the work connects to prevention, the tumor ecosystem, or treatment resistance and to patients.
• Missing the invitation-only structure. A rushed LOI can cost you the chance to submit a full proposal at all; the LOI deserves real effort.
• Waiting for the deadline to build the team. Cross-country collaborations and data agreements should be in place before you draft.

Frequently Asked Questions

How much can I receive? Up to $1 million per grant over three years, plus in-kind AWS cloud compute support.

Do I have to be based in the United States? No. International researchers at eligible non-profit institutions can apply, and the consortium explicitly spans the US, UK, Canada, and Australia.

Can a for-profit company apply? The program is aimed at non-profit universities, research hospitals, and recognized non-profit research facilities, not commercial entities.

Does everyone submit a full proposal? No. Applicants first submit a Letter of Intent; only selected teams are invited to submit a full proposal.

Where do I apply? Through SmartSimple, OCRA’s grant management system, linked from the official grant pages.

Is the current cycle still open? The 2026 LOI closed on June 23, 2026, and invited full proposals were due in September 2026. Watch OCRA’s grant pages for the next cycle’s dates, which have historically opened around World Ovarian Cancer Day in May.

Next Steps and Official Links

If your work sits at the intersection of ovarian or gynecologic cancer and modern data science, this is one of the most substantial awards you can pursue. Start by reading the current program description and RFP, confirm your institution’s eligibility, and map your idea against the three priority areas.

• Grant programs overview: https://ocrahope.org/research/information-for-researchers/grant-programs/
• How to apply for an OCRA grant: https://ocrahope.org/research/information-for-researchers/apply-for-a-grant/
• AI Accelerator Grant announcement: https://ocrahope.org/news/global-ovarian-cancer-research-consortium-ai-accelerator-grant-2026/
• Questions: [email protected]

Confirm all amounts, eligibility rules, and deadlines directly on OCRA’s official pages before you apply, since the exact terms and dates are set fresh for each annual cycle.