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NSF AI Planning Institute for Data-Driven Discovery in Physics is sponsored by National Science Foundation (NSF) (Carnegie Mellon University is a key participant). This planning institute aims to integrate cutting-edge AI methods into a broad range of physics areas, rapidly propagating successful methods and facilitating back-transfer from data-rich physics fields to AI development.
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NSF AI Planning Institute - NSF AI Planning Institute for Data-Driven Discovery in Physics - Carnegie Mellon University Welcome to the NSF AI Planning Institute for Data-Driven Discovery in Physics Welcome to the NSF AI Planning Institute for Data-Driven Discovery.
Physics applications of AI have led to some of the most exciting recent breakthroughs, from the synthesized radio imaging of the event horizon of a black hole (which used Machine Learning for image reconstruction) to explorations of the functioning of the human genome (Deep Learning for regulatory genomics and cellular imaging).
Scientists at CMU in departments such as Machine Learning and Statistics actively collaborate with scientists in the Department of Physics because of the opportunity for each field to spur development in the other. This collaboration is now accelerating, with weekly interactive seminars, now including AI scientists, astrophysicists, particle physicists, and an emerging team of biophysicists.
Our past work together spanning two decades, the recent excitement generated by these no-jargon seminars, and our recent NSF award emboldens us to propose a joint Physics/AI Planning Institute.
The aim is to bring cutting edge methods from AI into a broad range of physics areas, to rapidly propagate successful methods from one field of Physics to another thereby avoiding replication of effort, and to facilitate back-transfer from the data-rich sub-fields of physics to AI development.
The Planning phase will focus on areas where CMU scientists are already leaders, in which there are existing strong collaborations between physicists and AI researchers, and where rapid advances are being made: astrophysics, subatomic physics, and biophysics.
Applying AI will lead to significant advances in the areas of dark energy and galaxy formation; new ways of extracting information about the Higgs bosons and anomalies in gluon physics; and enhanced understanding of biological networks and predictions for cancerous tissues. Benefits in the other direction are clear as well: physics provides complex use cases and profound problems that motivate AI researchers to advance foundational AI.
NSF REU Applications are Now Closed Responsible AI in the Natural Sciences Workshop This material is based upon work supported by the National Science Foundation under the NSF AI Planning Institute for Data-Driven Discovery in Physics, grant number PHY2020295.
Any opinions, findings, and conclusions or recommendations expressed on this website are those of the participants and do not necessarily reflect the views of the National Science Foundation or the participating institutions.
According to the current listing, eligibility includes: Scientists at Carnegie Mellon University (CMU) in Machine Learning, Statistics, and Physics departments are actively collaborating. Opportunities may exist for researchers from CMU and potentially other institutions. Confirm the full requirements in the official notice before applying.
NSF AI Planning Institute for Data-Driven Discovery in Physics is funded by National Science Foundation (NSF) (Carnegie Mellon University is a key participant). 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.
The RFI closed June 22. Now the forum is standing up. AI Forge is a jointly governed, university-led venture funding interpretability, control, and adversarial robustness in one-year Project Ventures — here's how the 15 challenges, the CAISI tie, and the nonprofit administrator reshape who gets funded.
Read articleNSF 26-503, the CyberAICorps Scholarship for Service (CyberAI SFS), pays $27,000–$37,000 annual stipends plus full tuition for students who commit to government service in AI and cybersecurity, with institutional awards up to $2.5 million. The Scholarship Track closes July 21, 2026. Here's why placement infrastructure — not coursework — decides which universities win.
Read articleOn June 1, 2026, DARPA and the National Science Foundation jointly released the AI Forge initiative — a university-only forum, administered by a nonprofit launching summer 2026, that will fund Project Ventures of roughly $750,000 to $3 million over one-year terms in three thrust areas: AI interpretability, AI control, and adversarial robustness. The RFI on SAM.gov closes June 22 at 5pm ET and is restricted to U.S. universities and military service academies, one authorized submission per institution. Intellectual property is expected to be shared across forum participants, preferably through open-source licensing. This is the most significant joint DARPA-NSF research vehicle in a decade, and it is structured to bypass the frontier-lab model entirely.
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