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NSF Institute of Accelerated AI Algorithms for Data-Driven Discovery (A3D3) is sponsored by National Science Foundation (NSF). This institute targets fundamental problems in high-energy physics, multi-messenger astrophysics, and neuroscience. It focuses on developing and applying new AI-based solutions to analyze large datasets in real-time, enhancing discovery potential in these fields.
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Accelerated AI Algorithms for Data-Driven Discovery The National Science Foundation (NSF), under the Harnessing the Data Revolution (HDR) program, is providing funding to establish the Accelerated AI Algorithms for Data-Driven Discovery (A3D3) Institute, a multi-disciplinary and geographically distributed entity with the primary mission to lead a paradigm shift in the application of real-time artificial intelligence (AI) at scale to advance scientific knowledge and accelerate discovery.
Learn more about our mission A3D3 aims to construct the knowledge essential for real-time applications of artificial intelligence in three fields of science: high energy physics, multi-messenger astrophysics and systems neuroscience. The institute aims to develop customized AI solutions to process large datasets in real time, significantly enhancing the potential for discovery.
Hardware and Algorithm Co-development Developing AI methods to encode non-lattice-structured data is one main challenge in current AI systems. Build tools to process LHC collisions occurring 40 million times per second data in real-time using AI. Discover the computations that brain-wide neural networks perform to process sensory and motor information during behavior by using high-throughput and low-latency AI algorithms to process.
Study a mixture of hardware resources like CPU, GPU and FPGA required for ML science pipelines. Multi-messenger Astrophysics Process the data from telescopes, neutrino detectors, and gravitational-wave detectors to identify astronomical events corresponding to the most violent phenomena in the Cosmos. Develop common tools for deploying ML algorithms in dedicated science experiment systems implemented in FPGAs and ASICs.
Deming Chen named ACM Fellow Participate in the HDR ecosystem ML challenge A3D3 team leads the first end-to-end Machine Learning-based, real-time search for Binary Black Holes By: Erik KatsavounidisSeptember 26, 2025 A new, fully machine learning-based search method for binary black holes developed by members of the A3D3… Stay up to date, join our newsletter!
/* real people should not fill this in and expect good things – do not remove this or risk form bot signups */ We lead the paradigm shift of AI A3D3 aims to construct the knowledge essential for real-time applications of artificial intelligence in three fields of science: high energy physics, multi-messenger astrophysics and systems neuroscience.
The institute aims to develop customized AI solutions to process large datasets in real time, significantly enhancing the potential for discovery.
According to the current listing, eligibility includes: Not explicitly stated, but as an NSF AI Institute, it typically involves collaborations among research institutions and universities. Confirm the full requirements in the official notice before applying.
NSF Institute of Accelerated AI Algorithms for Data-Driven Discovery (A3D3) is funded by National Science Foundation (NSF). Verify program details on the funder's official page before applying.
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Economics of AI Fellowship is sponsored by Stripe. The fellowship supports foundational academic research in the economics of AI, an area currently understudied despite rapid technical progress in artificial intelligence. Fellows receive a baseline grant, opportunities to attend conferences with leading economists and technologists, and potential access to unique data via Stripe and its customers.
The UKRI Policy Fellowships 2025, funded by the Economic and Social Research Council, offer 18-month placements for academics to co-design research with UK government and What Works Network host organizations. Awards range from £180,000 to £280,000 and support three fellowship tracks: core policy fellows, Natural Hazards and Resilience policy fellows, and What Works Innovation fellows. Applicants must hold a PhD or equivalent research experience, be based at a UKRI-eligible UK organization, and possess relevant subject matter or methodological expertise. Government-hosted positions target early to mid-career academics, while What Works fellowships welcome all career stages. Fellows work directly with policymakers to bridge academic research and policy development on pressing national and global challenges. The application deadline is July 15, 2025.
NSF 26-516 folds Atmospheric and Geospace Sciences, Earth Sciences and Ocean Sciences into a single GEO Core solicitation: $110 million, 200 to 350 awards, proposals accepted anytime. Geosciences piloted no-deadline review nine years ago and knows what it does to submission volume. Here is the per-award math, the ship-time and permitting traps, and the two-year data-archive clock that now governs every award.
Read articleNSF 26-520 consolidates 14 mathematical sciences programs into $200 million and 400 to 600 awards with no per-PI cap and no deadline. Folded into it are Research Training Groups and Focused Research Groups — department-scale, cohort-building competitions that ran on a fixed annual date. Here is why removing that date is a different kind of change for training grants than for single-investigator grants, and what math departments should do before the fall.
Read articleNSF 26-522 consolidates AAG, ATI and MSIP into a single astronomical sciences solicitation with $100 million and about 140 awards. Accomplishment-based renewals will be returned without review, voluntary cost sharing is prohibited, and routine instrument upgrades are explicitly deprioritized. The new Innovations track — one to three awards a year — is where facility operations and community observing time now live.
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