Collaborative Research Development Grant – Microsoft AI for Good Lab

The Collaborative Research Development Grant – Microsoft AI for Good Lab is a specialized funding initiative offered by the Ovarian Cancer Research Alliance (OCRA) in partnership with Microsoft’s AI for Good Lab. This grant supports research into ovarian and related gynecologic cancers, with a required emphasis on artificial intelligence (AI) or machine learning (ML) methodologies. OCRA, the largest global organization focused exclusively on ovarian and gynecologic cancers, has invested $140 million in scientific research and supports over 95,000 individuals annually through free programming. Microsoft’s AI for Good Lab contributes to this effort by offering in-kind support via Azure cloud computing credits, enabling research teams to leverage advanced cloud-based AI infrastructure. The purpose of this grant is to stimulate innovative, collaborative, and interdisciplinary research that harnesses AI to improve understanding, detection, treatment, and prevention of ovarian cancer. Projects eligible for funding must demonstrate a central AI/ML component and show clear relevance to ovarian cancer or related gynecologic cancers. Priority research areas include early detection, etiology and prevention, therapeutic development, genetics, cancer biology, epidemiology, quality of life, and health services. A distinguishing feature of this program is the dual support model, combining OCRA's direct financial funding with technical resources from Microsoft. This is a collaborative team-based grant opportunity. A Principal Investigator (PI) must be designated, and teams can be drawn from either a single institution or multiple collaborating institutions. All lead investigators must be full-time faculty or hold equivalent roles at accredited academic or non-profit research institutions, with at least one investigator being a physician with expertise in ovarian or gynecologic cancers. Training positions are not eligible. Notably, there are no citizenship restrictions, and international applicants and collaborations are encouraged. OCRA will fund only non-profit entities such as research hospitals and universities; for-profit organizations and non-research nonprofits are ineligible. Grant funding is set at up to $300,000 per year for three years, totaling $900,000 per project. These funds are intended to support a broad range of research activities as described in the proposed work. Additionally, investigators will enter into a separate agreement with Microsoft to access Azure computing credits, which will support the AI/ML components of their research. The project start date is January 1, 2027, and successful applicants will be required to submit activation deliverables and annual narrative and financial progress reports. All publications resulting from this funding must credit OCRA, and peer-reviewed dissemination of findings is expected. Applications follow a two-step submission process beginning with a Letter of Intent (LOI). LOIs are due via the SmartSimple portal by 5:00 PM Eastern Time on March 25, 2026. All applicants will be notified of their status by mid-May 2026. Those invited to the next stage must submit a full proposal by mid-June 2026. Only about 14% of LOIs typically advance. LOIs must be submitted by the PI and include a scientific abstract, biosketches for all key personnel, and an estimated budget (a detailed budget is not required at this stage). Applicants should note that feedback on submitted LOIs or proposals will not be provided due to limited staff capacity. For technical questions related to the SmartSimple system, applicants are instructed to contact OCRA via [email protected]. Substantive program questions may be directed to Kristin McGowan at OCRA by phone at (212) 268-1002 or the same email. More information, including FAQs and eligibility criteria, is available on the OCRA website. This grant program is part of a broader mission to support cutting-edge cancer research through interdisciplinary innovation and philanthropic collaboration.

Highlight a robust AI or machine learning component with direct relevance to ovarian or gynecologic cancer; only 14% of LOIs are invited to submit full proposals.