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DARPA's TIAMAT (Transfer from Imprecise and Abstract Models to Autonomous Technologies) program develops AI methods that enable autonomous systems to transfer from low-fidelity simulations to real-world deployment, often described as the sim-to-real gap.
The program funds research on training reinforcement learning and imitation learning policies in inexpensive abstract simulators and then transferring those policies to physical platforms including ground vehicles, drones, and manipulators with minimal real-world data.
Technical focus areas include domain randomization, system identification, hybrid model-based learning, robust policy learning under distribution shift, and verification of transferred policies. TIAMAT runs in two 18-month phases with performer teams from universities, FFRDCs, and small businesses.
Strong fit for AI/ML and robotics researchers working on autonomous decision-making, simulation-trained agents, and verification of learned controllers for defense-relevant autonomous platforms.
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Or search similar grants →According to the current listing, eligibility includes: U.S. universities, nonprofit research organizations, federally funded R&D centers, and large and small businesses. Foreign organizations may participate as subawardees under specific arrangements. Confirm the full requirements in the official notice before applying.
The current listing shows individual awards in the $1 to $5 million range per performer across two 18-month phases. Recent awards include $1.2 million to University of Central Florida and multi-million-dollar awards to Johns Hopkins APL. Verify award ceilings, matching requirements, and allowable costs in the official notice.
DARPA TIAMAT for AI Sim-to-Real Transfer and Autonomous Systems is funded by Defense Advanced Research Projects Agency (DARPA). Verify program details on the funder's official page before applying.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
The NEH Humanities Research Centers on Artificial Intelligence program funds the creation of university-based humanities research centers focused on the ethical, legal, and societal implications of artificial intelligence. Funded centers undertake interdisciplinary humanities-led research that brings ethics, law, history, philosophy, anthropology, religious studies, literature, linguistics, and cultural studies to bear on questions raised by AI systems. Topics include responsible AI governance frameworks, AI and civil rights, AI and labor, cultural impact of generative AI, AI and creative authorship, philosophical foundations of machine reasoning, history of AI thought, and humanistic evaluation of AI safety and alignment. Centers are expected to convene researchers, train new humanities scholars in AI, host public-facing programming, and produce publications and translational tools that inform policy and public understanding. Strong fit for universities seeking to launch sustained interdisciplinary AI humanities research programs in partnership with computer science and other STEM departments.
NSF SaTC 2.0 (Security Privacy and Trust in Cyberspace) is the largest open solicitation for university-led cybersecurity research in the federal portfolio now expanded with AI security as an explicit priority area. The 2.0 reboot added generative AI security open-source software security quantum computing security and supply chain security as topics of interest addressing the bidirectional role of AI as both a cybersecurity threat and a defensive tool. Research awards support adversarial machine learning and attacks on AI systems AI weaponization against people information and systems privacy-preserving machine learning and responsible AI use for detecting and responding to cyber threats. The program funds three award types: Research awards up to $1.2M for four years Education awards up to $500K for three years and Seedling awards up to $300K for two years through Dear Colleague Letters. Proposals are accepted on a recurring annual basis with two windows per year. This is distinct from NSF CyberAICorps which focuses on scholarship and workforce development and from NSF AIMing which focuses on AI formal methods and mathematical reasoning.
The Air Force Research Laboratory Information Directorate's Geospatial Intelligence Processing and Exploitation (GeoPEX) Broad Agency Announcement, FA8750-21-S-7006, is an open two-step BAA soliciting white papers for research, development, integration, test, and evaluation of technologies and techniques to provide geospatial intelligence (GEOINT) in all its forms and from whatever source, including imagery, imagery intelligence, and geospatial data. Explicit focus areas include AI/ML techniques for full-spectrum GEOINT, multi-INT data fusion, cloud-based high-performance computing for geospatial analytics, photogrammetry, computer vision for overhead imagery, automated target recognition, change detection, multi-modal foundation models for geospatial data, and edge AI for tactical reconnaissance. The BAA is open and effective until 30 September 2026 with rolling white paper submission; only white papers are accepted as initial submissions and formal proposals are accepted by invitation only. Strong fit for AI and computer vision performers building geospatial analytics, foundation models, or autonomous reconnaissance tools for Air Force, intelligence community, and combatant command users.
DPA26BZ06-DV023 is a Direct-to-Phase-II SBIR paying $700,000 over 18 months plus a $500,000 option. The physics demands 256x more transmit power than the systems that qualify you to compete, and DARPA will not accept modeling alone as proof. Here is the eligibility wall, the five engineering problems, and who can realistically win it before the October 21 close.
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Read articleDPA26BZ06-DV026 offers $300,000 at Phase I or $1,800,000 as a single Direct-to-Phase-II tranche with no options. The deliverable is a simulated auction market that measures whether AI agents deceive, collude, or manipulate the humans they serve — measured entirely from the outside. Here is the 90% efficiency gate, the team composition most bidders will get wrong, and why this topic sits in DARPA's biology office.
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