Search and apply for every grant in existence.

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. The National Science Foundation Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and Social, Behavioral and Economic Sciences (SBE) will jointly sponsor research collaborations consisting of mathematicians, statisticians, computer scientists, engineers, and social and behavioral scientists focused on the mathematical and theoretical foundations of AI. Research activities should focus on the most challenging mathematical and theoretical questions aimed at understanding the capabilities, limitations, and emerging properties of AI methods as well as the development of novel, and mathematically grounded, design and analysis principles for the current and next generation of AI approaches. Specific research goals include: establishing a fundamental mathematical understanding of thefactors determining the capabilities and limitations of current and emerging generations of AI systems, including, but not limited to, foundation models, generative models, deep learning, statistical learning, federated learning, and other evolving paradigms; the development of mathematically grounded design and analysis principles for the current and next generations of AI systems; rigorous approaches for characterizing and validating machine learning algorithms and their predictions; research enabling provably reliable, translational, general-purpose AI systems and algorithms; encouragement of new collaborations in this interdisciplinary research community and between institutions. The overall goal is to establish innovative and principled design and analysis approaches for AI technology using creative yet theoretically grounded mathematical and statistical frameworks, yielding explainable and interpretable models that can enable sustainable, socially responsible, and trustworthy AI. Funding Opportunity Number: 24-569. Assistance Listing: 47.041,47.049,47.070,47.075. Funding Instrument: G. Category: ST. Award Amount: $500K – $1.5M per award.

This Notice of Funding Opportunity (NOFO) invites applications for a Research Coordinating Center (RCC) to participate in a consortium of clinical centers that will test anti-obesity medication (AOM) treatment strategies for youth with obesity that maximize benefits and minimize risks of AOM use. Such intervention strategies should support the promotion of healthy growth and development; adequate nutritional status/intake, healthy eating and physical activity behaviors; mental health and well-being (e.g., body image, self-esteem, mood, etc.), and quality of life and be feasible to implement in clinical care settings. Priority areas include testing strategies to determine optimal developmental stage for AOM initiation, rate and amount of weight loss, AOM class, dose, frequency, and duration, and content and intensity of adjunct lifestyle therapies that may be imperative to ensure normal psychological and physical development and to potentially avoid lifelong dependence on AOMs. Investigators should also evaluate potential predictors of response/ nonresponse to various treatment strategies under evaluation. The clinical centers may conduct independent or multicenter trials but will collaborate on the development of protocols, use of common measures and data elements, use of a central laboratory and standardized procedures to collect data and biospecimens, and data analyses and manuscriptsThe RCC will lead, manage, and harmonize efforts for the Consortium including 1) providing management and administrative support; 2) providing leadership and expertise on statistical design and analysis, 3) providing research coordination with a central laboratory, 4) harmonizing data collection methods and use of common data elements, 5) developing the database; 6) conducting data management and data analyses for Consortium studies; and 7) fostering research collaborations. This NOFO uses a cooperative agreement mechanism (U24) and runs in parallel with a companion NOFO (RFA-DK-27-121). Funding Opportunity Number: RFA-DK-27-136. Assistance Listing: 93.847. Funding Instrument: CA. Category: HL. Award Amount: $1M total program funding.

The Rural Utilities Service (RUS or the Agency), a Rural Development (RD) Agency of the United States Department of Agriculture (USDA), is soliciting Letters of Interest (LOI) for loan Applications, announcing the Application process for those loans, and providing deadlines for Applications from eligible entities under the Powering Affordable Reliable Technology (PART) Energy Program. These loan funds will be made to qualified PART Applicants to finance power generation Projects for Renewable Energy Resource (RER) systems or Energy Storage Systems (ESS) that support RER Projects. The PART Program is making approximately $410 million in appropriated budget authority (BA) funding available under the Inflation Reduction Act of 2022 (IRA). RUS will process and evaluate complete LOI on a rolling basis in the order they are received. The PART Program is to be carried out by the RUS pursuant to Section 22001 of the IRA. Section 22001 of the IRA amends Section 9003 of the Farm Security and Rural Investment Act of 2002 by adding new subsection (h). Section 22001 of the IRA provides RUS with appropriated funds “for the cost of loans under Section 317 of the RE Act.” Additionally, Section 22001 of the IRA provides that PART funds may be utilized to finance Projects that store electricity generated from eligible energy sources listed under Section 317 of the RE Act. These Project Loans or System Loans will be forgiven up to forty percent (40%), provided the Awardee and the Project otherwise meet the terms and conditions of the loan forgiveness. Approximately $410 million in budget authority is being made available under this Notice. The Administrator reserves the right to increase this funding level should additional funds become available or reduce funding in the event an insufficient number of high-quality projects are submitted for consideration. Cost Sharing or Matching: (a) Project Loans. Awards will finance up to 75% of the total capitalized costs of a Project. Awardees will be required to provide at least 25% of the Project’s total capitalized cost in the form of cash or equity investments, which may not be derived from debt instruments. However, the Agency may utilize its authority under Section 306F of the RE Act and finance up to 100% of the costs of the Projects benefiting SUTA areas. (b) System Loans. PART System Loans may cover up to 100% of the total costs of the Project. Funding Opportunity Number: RUS-PART-2026. Assistance Listing: 10.757. Funding Instrument: O. Category: EN. Award Amount: $1M – $100M per award.