Research Education: Short Courses on Alzheimer's Disease and Related Dementias for the Behavioral and Social Sciences (R25 Clinical Trial Not Allowed) (Discontinued)

The FY2026 Department of Defense Multidisciplinary University Research Initiative (MURI) program supports basic research in science and engineering at U.S. institutions of higher education, with emphasis on multidisciplinary research where more than one traditional discipline interacts. The Army, Navy, and Air Force basic research offices are seeking applications across 22 topic areas including artificial intelligence and autonomy, information sensing and processing, and systems manipulation. MURI grants typically provide $1.25 million to $1.5 million per year for three years with option to extend two additional years. Approximately $170 million in total funding is available annually across all topics. The program is administered through the Office of Naval Research (ONR), Army Research Office (ARO), and Air Force Office of Scientific Research (AFOSR).

The NSF Convergence Accelerator is a grant from the National Science Foundation (NSF) that funds multidisciplinary teams working to solve national-scale societal challenges through convergence research and innovation. Launched in 2019 under NSF's Directorate for Technology, Innovation and Partnerships, the program operates in two phases: Phase 1 awards are up to $750,000, with successful teams advancing to larger Phase 2 awards. Eligible applicants include institutions of higher education and nonprofit or for-profit organizations. Track I and Track K focus on specific high-priority topics announced each funding cycle. The next deadline is June 15, 2026. Proposals must comply with updated NSF research security policies effective July 2025.

SCH is the flagship interagency vehicle for biomedical AI in the United States and its defining feature is that it is jointly written, jointly reviewed and jointly funded by NSF and NIH - three NSF directorates alongside more than twenty NIH Institutes and Centers. That structure has a direct consequence for applicants: a proposal has to satisfy two review cultures at once. It must advance the computational or engineering science in a way NSF recognises as fundamental, and it must address a pressing biomedical or public health question in a way NIH programme staff recognise as consequential. Proposals that read as a health application of off-the-shelf machine learning tend to fail the first test; proposals that read as methods work with a health dataset attached tend to fail the second. The solicitation explicitly seeks transformative high-risk, high-reward advances in computer and information science, engineering, mathematics, statistics, and behavioural or cognitive research, and the strongest submissions are genuinely co-designed by computational and clinical investigators from the outset rather than assembled from a methods group and a clinical collaborator late in the process. Award sizes vary considerably with project type, running from roughly USD 300,000 for more focused efforts up to USD 2,000,000 for larger integrative projects, commonly over four to five years, with NSF investing on the order of USD 15,000,000 to USD 20,000,000 annually and participating NIH Institutes adding further funds. Because so many NIH Institutes participate, alignment with a specific Institute's mission is a practical determinant of whether a strong proposal finds a funding home, and early contact with both NSF and relevant NIH programme officers is the single highest-value step before submission. The programme operates on an annual target date, with 10 September 2026 as the next opportunity.

Under DOE's new Genesis Mission umbrella, the Office of Technology Commercialization opened an SBIR/STTR Phase I opportunity providing non-dilutive funding to small businesses across four AI-driven topic areas: biotechnology, quantum computing, materials design, and autonomous laboratories. DOE intends to make roughly 40 awards totaling about $10 million in Phase I (approximately $250,000 each), representing up to ~$44 million in cumulative funding across the full project lifecycles. Project pitches were due September 10, 2026.

The NSF Computer and Information Science and Engineering: Future Computing Research (Future CoRe) program (NSF 25-543) supports foundational computing research and education with a strong emphasis on AI and machine learning. The program funds research that advances the foundations of computing including AI/ML theory, algorithms, systems, and applications. Future CoRe supports small to medium-sized research projects that can have significant impact on computing foundations. The program has target dates of February 5, 2026 and September 10, 2026 for proposal submissions (note: these are target dates, not deadlines, meaning proposals may be submitted at any time but will be reviewed in batches around these dates). Investigators may not serve as PI, co-PI, or Senior/Key Personnel on more than two proposals submitted within any consecutive 12-month period across all Future CoRe programs. This is one of NSF's primary mechanisms for funding fundamental AI and machine learning research at U.S. academic institutions, covering areas such as machine learning theory, natural language processing, computer vision, robotics foundations, and human-AI interaction.

CISE Future Computing Research (Future CoRe, NSF 25-543) is the National Science Foundation's core foundational research program for computer and information science, and it is the single most accessible NSF pathway for investigator-initiated AI and machine learning research. The solicitation spans three participating divisions - Computing and Communication Foundations, Information and Intelligent Systems, and Computer and Network Systems - which together cover machine learning theory, algorithms, foundation model architectures, natural language processing, computer vision, robotics and intelligent systems, and the systems and hardware substrate that large-scale AI training and inference depend on. Awards run up to $1 million over four years, with typical projects landing between $150,000 and $250,000 per year. Unlike NSF's large center-scale AI competitions, Future CoRe funds single investigators and small teams, which makes it the realistic target for a faculty member starting or sustaining a generative AI research agenda. The program has two annual submission windows: the first Thursday in February and the second Thursday in September, with the September 2026 target date falling on 10 September 2026. Proposals may indicate the extent to which generative AI was used in preparing the proposal, per NSF guidance.

STREAM - Systems for Tracking and Resilience in Efficient Agricultural-input Management - is an ARPA-H program sitting at the intersection of agriculture, environmental monitoring, and human health. It funds the discovery of safer herbicide alternatives, the tracking of how herbicides spread through soil and water, and methods to remove or degrade herbicide residues from the environment. Artificial intelligence and machine learning are central to the discovery half of the program: ARPA-H is explicitly seeking AI/ML approaches to identify novel chemical compounds, biologically derived herbicides, and advanced biological treatments that target weeds while minimising ecosystem impact - a generative and predictive chemistry problem closely analogous to AI-driven drug discovery, applied to agricultural inputs rather than therapeutics. The health framing matters for positioning a proposal: ARPA-H's interest is in the downstream human health consequences of agricultural chemical exposure, so proposals need to connect the agricultural or environmental technology to a health outcome rather than to yield or cost alone. STREAM is solicited through the Scalable Solutions Office Innovative Solutions Opening, ARPA-H-SOL-24-105, on a rolling basis: applicants submit a Solution Summary first and receive feedback before full proposals are invited. The stated deadline is 14 September 2026 at 11:59pm ET. Award amounts are not fixed and are negotiated as Other Transaction agreements; the range given here is indicative of ARPA-H mission office awards generally, not a published STREAM ceiling.

RFA-LM-26-004 is the National Library of Medicine's institutional training grant program for building the research workforce in biomedical informatics, data science, and artificial intelligence. NLM funds universities and research institutions to run predoctoral and postdoctoral training programs that prepare researchers to apply computational and AI/ML methods to health and biomedical problems - clinical decision support, medical imaging analysis, electronic health record modeling, drug discovery informatics, and public health data science among them. The program is one of the most durable pipelines in health AI: NLM institutional training grants have seeded a large share of the academic biomedical informatics faculty in the United States. The FY2026 competition makes $12,000,000 available across approximately 25 awards, with individual awards from $500,000 to $775,000. Because this is a T-series institutional mechanism rather than a research project grant, the application is built around program design, mentor quality and depth, trainee recruitment and retention plans, and curriculum integration across computer science, statistics, and biomedical disciplines - not around a specific research aim. Institutions with an existing informatics or computational biology program and a demonstrable mentor pool are the realistic applicants. The estimated post date is 2 June 2026 with applications due 25 September 2026, awards in January 2027, and project start on 1 July 2027.

The Geospatial Intelligence Processing and Exploitation (GeoPEX, FA8750-21-S-7006) Broad Agency Announcement, issued by the Air Force Research Laboratory Information Directorate (Rome, NY), funds research, development, integration, test, and evaluation of technologies that enhance geospatial intelligence (GEOINT) capabilities. AI/ML techniques are a core focus area alongside full-spectrum GEOINT, multi-INT data production, cloud-based high-performance computing, and photogrammetry, supporting automated target detection, sensor fusion, and exploitation of overhead imagery and full-motion video. This is an open, two-step BAA: only white papers are accepted as initial submissions, and formal proposals are by invitation only. The announcement is effective through September 30, 2026, with approximately $99.9 million available and individual awards from $250,000 to $10 million.

DARPA's Biological Technologies Office (BTO) Office-Wide Broad Agency Announcement (HR001126S003) seeks abstracts and proposals that leverage biological properties and processes to revolutionize the ability to protect warfighters, with an explicit emphasis on applying artificial intelligence and machine learning across biological science. Priority research areas include machine learning and AI applications, combat casualty care, human performance optimization, materials/sensors/processing, agricultural and environmental solutions, security and surveillance, and biomedical and biodefense initiatives. Abstracts are accepted on a rolling basis until September 30, 2026.

DE-FOA-0003600 is the DOE Office of Science's FY 2026 open, rolling solicitation for financial assistance, providing roughly $500 million across seven program areas: Advanced Scientific Computing Research, Basic Energy Sciences, Biological and Environmental Research, Fusion Energy Sciences, High Energy Physics, Nuclear Physics, and Isotope R&D and Production. AI and machine learning research is supported directly through Advanced Scientific Computing Research, while Biological and Environmental Research funds atmospheric process research, environmental systems process research, and earth-energy systems modeling — making this a major channel for AI-enhanced climate and earth system modeling work. DOE anticipates 200 to 350 new awards ranging from $5,000 to $5 million each, with project periods from six months to five years. The FOA opened September 30, 2025 and accepts applications on a rolling basis through the end of FY 2026.

GeoPEX is AFRL's standing vehicle for geospatial intelligence research and it is unusually well-suited to AI teams because the problem set - turning imagery and geospatial data into intelligence at scale - is fundamentally a machine learning problem. The BAA seeks research, development, integration, test and evaluation of technologies and techniques that provide GEOINT in all its forms and from whatever source, whether imagery, imagery intelligence, or geospatial data and information. Named focus areas include Full-Spectrum GEOINT, AI and machine learning techniques, multi-peak data production, cloud-based high-performance computing, and photogrammetry. The funding envelope is substantial and the range is wide: individual awards run USD 250,000 to USD 10,000,000 over periods normally not exceeding 36 months, against estimated total programme funding of approximately USD 99,900,000 - which means both a focused university effort and a large integrated industry programme can find a home here. Submission mechanics are the standard AFRL two-step and should be planned for accordingly: only white papers are accepted as initial submissions and formal proposals are accepted by invitation only, so the white paper is the whole competition from the proposer's perspective. Award instruments include procurement contracts, grants, cooperative agreements and other transactions, which gives flexibility on the university side. Submissions require Standard Form 424 and a Security Program Questionnaire, and awards are subject to science and technology protection requirements under AFRLI 61-113 and to FASCSA compliance. The BAA is open and continuous with white papers accepted through 30 September 2026; the BAA programme manager contact is afrl.rig.geopex@us.af.mil.