Pivot Database
Pivot is a comprehensive, searchable database that connects UF faculty, staff, students, and researchers with funding opportunities and expertise. Available at no cost to the UF community, Pivot brings together research opportunities, funding sources, and global collaborations to enhance your research impact.
Pivot AI Listings
Below is a selection of curated Pivot searches within AI. Please scroll to see all listings within the respective categories.
Natural Sciences
Medicine
Foundational/Machine Learning
Engineering & Tech Development
Digital Twins
Business and Social Systems
Featured Funding Opportunities
The National Endowment for the Humanities (NEH) Division of Research is accepting applications for the Humanities Research Centers on Artificial Intelligence program. The purpose of this program is to support the establishment of new collaborative humanities research centers focused on gaining a clearer understanding of AI and its implications for the United States. A center is a sustained collaboration among multiple scholars focused on exploring the humanities implications of AI through two or more related scholarly activities.
Maximum award amount is us to $750,000 ($500,000 in outright funds plus $250,000 in Federal Matching Funds)
An updated solicitation for the Smart Health and Biomedical Research in the Era of Artificial Intelligence and Advanced Data Science (SCH) program is now available. Important changes are summarized at the beginning of the solicitation (NSF 25-542). The SCH Program supports the development of transformative high-risk, high-reward advances in computer and information science, engineering, mathematics, statistics, behavioral, and/or cognitive research to address pressing questions in the biomedical and public health communities. Please see the program webpage for full information. Questions should be directed to sch-correspondence@nsf.gov.
Due: Proposal Deadline October 3, 2025
Machine Learning and Artificial Intelligence (AI) are enabling extraordinary scientific breakthroughs in fields ranging from protein folding, natural language processing, drug synthesis, and recommender systems to the discovery of novel engineering materials and products. These achievements lie at the confluence of mathematics, statistics, engineering and computer science, yet a clear explanation of the remarkable power and also the limitations of such AI systems has eluded scientists from all disciplines. Critical foundational gaps remain that, if not properly addressed, will soon limit advances in machine learning, curbing progress in artificial intelligence. It appears increasingly unlikely that these critical gaps can be surmounted with increased computational power and experimentation alone. Deeper mathematical understanding is essential to ensuring that AI can be harnessed to meet the future needs of society and enable broad scientific discovery, while forestalling the unintended consequences of a disruptive technology.
Due: October 10, 2025
| Autonomous experimentation is poised to accelerate research and unlock critical scientific advances that bolster U.S. competitiveness and address pressing societal needs. Programmable Cloud Laboratories are able to execute automated workstreams, including self-driving lab workflows, to efficiently move research goals through artificial intelligence (AI) enabled experiment design, laboratory preparations, data collection, data analysis and interpretation. While limited-scale efforts have shown promise, versatile programmable and self-driving labs capable of addressing complex research questions with trustworthy results will require coordinated technological advances and an engaged research community. |
Due: October 20, 2025
Annual Funding Opportunities
A key focus of the design of modern computing systems is performance and scalability, particularly in light of the limits of Moore’s Law and Dennard scaling. To this end, systems are increasingly being implemented by composing heterogeneous computing components and continually changing memory systems as novel, performant hardware surfaces. Applications fueled by rapid strides in machine learning, data analysis, and extreme-scale simulation are becoming more domain-specific and highly distributed. In this scenario, traditional boundaries between hardware-oriented and software-oriented disciplines are increasingly blurred.
Due: Fourth Monday in January annually.
The Division of Mathematical Sciences (DMS) in the Directorate for Mathematical and Physical Sciences (MPS) at the National Science Foundation (NSF) and the Air Force Office of Scientific Research (AFOSR) plan to jointly support foundational mathematical and statistical research on Digital Twins in applied sciences. Recent years have witnessed a significant increase in the demand and interest in applications that involve collaborative teams developing and analyzing Digital Twins to support decision making in various fields, including science, engineering, medicine, urban planning, and more. Both agencies recognize the need to promote research aiming to stimulate an interplay between mathematics/statistics/computation and practical applications in the realm of Digital Twins. This program encourages new collaborative efforts within the realm of Digital Twins, aiming at stimulating fundamental research innovation, pushing, and expanding the boundaries of knowledge, and exploring new frontiers in mathematics and computation for Digital Twin development, and its applications. By leveraging this synergy, the program aims to harness science, technology, and innovation to address some of our Society’s most pressing challenges.
Due: March 15th annually
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