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The Oberndorf AI Medical Catalyst Grant funds University of Florida medical students to carry out research applying artificial intelligence to medical problems, with each award set at 5,000 US dollars. It sits within the Oberndorf AI Medical Scholarship Program, endowed by a donation from Lou and Rosemary Oberndorf and run jointly by the UF College of Medicine Office of Research and the Intelligent Clinical Care Center, known as IC3.
The programme's design reflects a specific view of what limits clinical AI training: it does not require applicants to have prior AI experience, but it does require that each project have an AI mentor who is a current IC3 member or has membership pending and who brings extensive AI experience.
In other words, the money follows the mentorship rather than the credential, which makes the award genuinely accessible to clinically oriented students who have the interest but not yet the technical background.
Eligible project scope spans clinical decision-making, diagnostics, research and healthcare management, and past awardees have worked on large language models over electronic health record data, predictive analytics for kidney disease, and AI image segmentation in medical imaging. The most recently published cycle closed on 5 January 2026 with submission through the University of Florida InfoReady platform.
No deadline is recorded here because the next cycle's date has not been published. For UF medical students, this is the designated on-ramp into AI research; for everyone else it is a useful model of how an institution can lower the barrier to clinical AI training.
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Or search similar grants →According to the current listing, eligibility includes: This award is restricted to second, third and fourth-year medical students currently enrolled at the University of Florida, so first-year students, students at other institutions, residents, fellows and faculty are not eligible. The restriction is absolute and there is no indication of exceptions, which means the grant has no relevance to applicants outside UF beyond its value as a programme design precedent. Prior experience with artificial intelligence is explicitly not required, but a strong demonstrated interest in AI and medicine is described as essential, and the substantive gate is mentorship: each application must identify an AI mentor who is either a current member of the Intelligent Clinical Care Center or has IC3 membership pending, and who has extensive AI experience. Students should therefore secure the mentor before writing the proposal, since the mentorship relationship is effectively the eligibility criterion that carries weight. Proposals must advance innovative AI approaches to medical challenges, with eligible areas including clinical decision-making, diagnostics, research and healthcare management, so purely technical machine-learning work with no articulated medical problem is a poor fit. Submission is through the University of Florida InfoReady platform. The most recently published cycle closed on 5 January 2026 as part of the FY25-26 programme year, and no deadline is recorded here because the subsequent cycle's date had not been announced at the time of recording. Prospective applicants should check the IC3 research pages for the current call and confirm award terms, which may change between cycles. Confirm the full requirements in the official notice before applying.
The current listing shows each Oberndorf AI Medical Catalyst Grant is 5,000 US dollars, and because that is a fixed per-award figure rather than a range, amount_min and amount_max are both recorded as 5,000. The number of awards per cycle is not published. This is a seed or catalyst award in the literal sense: 5,000 dollars does not fund a research programme, and applicants should treat it as support for a defined, short-horizon student project, typically covering data access or computing costs, a summer stipend component, software, or conference dissemination. It is not a route to funding faculty effort or substantial infrastructure. The practical value is disproportionate to the dollar figure for the target population, because the binding constraint on medical-student AI research is usually not money but access to a qualified mentor, and the programme addresses that directly by requiring an AI mentor who is an IC3 member or has membership pending. Applicants should therefore budget conservatively and invest their effort in the mentorship pairing and project scoping rather than in constructing an elaborate budget. Prior awardee projects give a realistic sense of scale: large language models applied to electronic health record data, predictive analytics for kidney disease, and AI image segmentation for medical imaging. Because the award is institutionally restricted and modest, it is best understood as a stepping stone toward larger extramural funding rather than a funding target in itself. Verify award ceilings, matching requirements, and allowable costs in the official notice.
University of Florida Oberndorf AI Medical Catalyst Grant for Medical Student Research Applying Artificial Intelligence to Clinical Care and Diagnostics is funded by Oberndorf AI Medical Scholarship Program, funded by Lou and Rosemary Oberndorf, administered by the University of Florida College of Medicine Office of Research and the Intelligent Clinical Care Center (IC3). Verify program details on the funder's official page before applying.
This opportunity targets applicants in Florida. If your organization operates elsewhere, check the official notice for location requirements.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
The CU System Sprint Grant is a small, fast-turnaround internal award supporting faculty who want to develop and implement AI-driven pedagogical strategies within a single course. At up to $20,000 with an explicit allowance for one course buyout, it is designed around the real obstacle facing most faculty experimenting with generative AI in teaching - not equipment or data, but protected time. The single-course framing is a deliberate scoping decision and applicants should respect it: proposals aimed at curriculum-wide or program-level transformation are a poor fit, while a concrete plan to redesign one course around AI tools and measure the effect on student learning outcomes matches what the program is asking for. Eligibility extends to tenured and tenure-track faculty as well as full-time and instructional series faculty across all CU campuses, which is broader than many internal research awards and deliberately includes teaching-focused appointments who are often closest to the pedagogical questions at issue. This is the second of three planned cycles, so faculty who miss the October 16, 2026 deadline should expect a third round. For a grants database this entry is narrower in eligibility than a federal or foundation program - it is open only to CU faculty - but it is a useful example of the institutional micro-grant tier that has grown rapidly as universities respond to generative AI in the classroom, and comparable programs now exist at many university systems. Faculty at other institutions who find this relevant should check whether their own provost or academic affairs office runs an equivalent scheme, since these internal AI teaching grants are frequently under-advertised and undersubscribed relative to external competitions.
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