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Find similar grantsAI2050 Fellowship is sponsored by Schmidt Sciences. Provides multi-year fellowships for early-career researchers pursuing transformative work on ensuring AI benefits humanity, focusing on AI safety, alignment, robustness, and beneficial AI deployment.
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Fellows Community - AI2050 The AI2050 Senior Fellowship supports established leaders who have made significant contributions to their field. Senior Fellows are highly accomplished individuals working on approaches for increasing the beneficial promise of AI. The breadth of their collective projects showcases the range of work that will be critical to answer the AI2050 motivating question.
A new class of Senior Fellows is awarded each year. Senior Fellows receive a three year award to work on specific projects that advance the AI2050 motivating question. Their projects are often ambitious, hard-to-fund through traditional sources, interdisciplinary, and innovative.
AI2050 generally reserves 5 awards each year, but in some cycles has had such an extraordinary pool that the initiative gives more than five awards. AI2050 Senior Fellows are selected using a closed nominations process, so there is no application.
Professor, University of Toronto Associate Professor, Stanford University Professor, Flatiron Institute & New York University Professor, University of Toronto Professor, University of California at Berkeley Professor, University of Oxford Professor, University of Washington 2025 Early Career Fellows The AI2050 Early Career Fellowship is an enabling fellowship to encourage postdoctoral and pre-tenure researchers from around the world to pursue bold and ambitious work on hard problems in AI, which often is multidisciplinary and typically hard to fund.
Each year, AI2050 generally reserves 15 awards, but in some cycles the initiative has had such an extraordinary pool that it names more than fifteen fellows. Early Career Fellows receive an award to support a three year project that advances their research and receive non-monetary support, such as connections to stakeholders to help them advance their research and amplify its results.
AI2050 Early Career Fellows are selected using a closed nomination process, so there is no application.
Assistant Professor, Mila Quebec AI Institute Incoming Assistant Professor, Harvard University Assistant Professor, École Polytechnique Fédérale de Lausanne Assistant Professor, École Polytechnique Fédérale de Lausanne Assistant Professor, University of Cambridge Assistant Professor, Princeton University Assistant Professor, University of Pennsylvania Assistant Professor, Stanford University Assistant Professor, University of Washington Assistant Professor, Arizona State University Incoming Assistant Professor, New York University Assistant Professor, Indian Institute of Technology–Madras Incoming Assistant Professor, Massachusetts Institute of Technology Assistant Professor, École Polytechnique Fédérale de Lausanne Assistant Professor, Northeastern University Associate Professor, Massachusetts Institute of Technology Assistant Professor, Harvard University Christian Schroeder de Witt Research Fellow, University of Oxford Assistant Professor, Princeton University Assistant Professor, National University of Singapore Daniel (1972) and Gail Rubinfeld Professor, Massachusetts Institute of Technology Dieter Schwarz Foundation Professor, Stanford University Ron and Antonia Nielsen Professor, Cornell University Associate Professor, University of Toronto Ashall Professor, University of Oxford 2024 Early Career Fellows Assistant Professor, Massachusetts Institute of Technology Assistant Professor, Cornell University Assistant Professor, Carnegie Mellon University Assistant Professor, University of Washington Assistant Professor, Massachusetts Institute of Technology Assistant Professor, Johns Hopkins University Associate Professor, Massachusetts Institute of Technology Associate Professor, Massachusetts Institute of Technology Assistant Professor, University of Washington Assistant Professor, Carnegie Mellon University Associate Professor, University of Oxford ELLIS group leader, Max Planck Institute for Intelligent Systems Assistant Professor, Indian Institute of Technology Bombay Assistant Professor, Mila Quebec AI Institute Assistant Professor, University of California San Diego Assistant Professor, University of Toronto Presidential Postdoctoral Fellow, Nanyang Technological University Assistant Professor, Stanford University Assistant Professor, University of Pennsylvania Assistant Professor, University of Wisconsin, Madison Bren Professor, California Institute of Technology Professor, University of Cape Town Kusama Professor of Art, Stony Brook University Bloomberg Distinguished Professor, Johns Hopkins University Sequoia Professor, Stanford University Professor, Massachusetts Institute of Technology 2023 Early Career Fellows Assistant Professor, UC Berkeley Assistant Professor, George Washington University Assistant Professor, Massachusetts Institute of Technology Assistant Professor, Stanford University Assistant Professor, UCLA Assistant Professor, UC Berkeley Executive Director, Center for AI Safety Assistant Professor, Carnegie Mellon University Assistant Professor, Harvard University Katarzyna Nowaczyk-Basińska Research Fellow, University of Cambridge Assistant Professor and Canada CIFAR AI Chair, University of Toronto Assistant Professor, University of California, Berkeley Assistant Professor, Indian Institute of Science (IISc), Bangalore Assistant Professor, University of Pennsylvania Assistant Professor, UC Berkeley Assistant Professor, Georgia Institute of Technology Assistant Professor, Imperial College London Assistant Professor, University of Chicago Jerry Yang and Akiko Yamazaki Professor, Stanford University Associate Professor, Stanford University Andrew (1956) and Erna Viterbi Professor, Massachusetts Institute of Technology Professor, University of Oxford 2022 Early Career Fellows Assistant Professor, Marist University Associate Professor, Massachusetts Institute of Technology Assistant Professor, Princeton University Assistant Professor, Northeastern University Assistant Professor, Massachusetts Institute of Technology Assistant Professor, Northwestern University Associate Scientist, Fermi National Accelerator Laboratory Assistant Professor, Carnegie Mellon University Lecturer, Imperial College London Assistant Professor, University of Toronto Assistant Professor, Carnegie Mellon University Assistant Professor, Cornell University Senior Lecturer, King's College London Associate Professor, Syracuse University Assistant Professor, University of Illinois Urbana-Champaign
Key questions and narrative sections extracted from the solicitation.
Aim 1: Characterize and forecast misalignment in frontier AI systems
Aim 2: Develop generalizable measurements and interventions
Aim 3: Oversee AI systems with superhuman capabilities and address multi-agent risks
Scoring criteria used to review proposals for this grant.
According to the current listing, eligibility includes: Early-career researchers globally. Confirm the full requirements in the official notice before applying.
The current listing shows up to $500,000. Verify award ceilings, matching requirements, and allowable costs in the official notice.
AI2050 Fellowship is funded by Schmidt Sciences. Verify program details on the funder's official page before applying.
Yes — this listing is flagged as national in scope, so applicants across the U.S. may apply, subject to the sponsor's other eligibility criteria.
Applications go through the funder's official portal — the Apply Now link on this page goes there directly.
Schmidt Sciences invites proposals for the 2026 Science of Trustworthy AI RFP, funding technical research that advances the science of building trustworthy AI systems. The program addresses three interconnected research aims: understanding why frontier AI systems develop misaligned goals that fail under distribution shift or pressure (Aim 1), creating valid evaluations and interventions to control what AI systems learn (Aim 2), and developing oversight mechanisms for superhuman AI capabilities and managing multi-agent risks (Aim 3). Beyond direct funding, awardees receive computing resources including GPUs and CPUs, software engineering support, API credits with frontier model providers, and access to a research community. The program is open globally and encourages cross-institutional and cross-geographic collaborations. Indirect costs are capped at 10% of total direct costs.
The VESRI Climate Modeling Challenge is a Schmidt Sciences call, run through the Virtual Earth System Research Institute, that funds research teams to make coupled climate models faster to improve, more reproducible and more accurate. The challenge will fund up to five teams with up to 2 million US dollars each over 24 months to implement and test new methods, explicitly including machine learning, improved representation of physical processes, and advanced calibration workflows. Expressions of intent were invited through 11 September 2026, with a full proposal stage to follow for shortlisted teams. The framing matters: VESRI is targeting the engineering bottleneck in climate modelling rather than climate science questions as such. Coupled model development cycles are slow because calibration is expensive and model updates are hard to reproduce, and the challenge asks teams to demonstrate methods that shorten that loop. Proposals that treat machine learning as an end in itself, rather than as a means to faster and more reproducible model iteration, are mismatched to the brief. VESRI already coordinates hundreds of climate and data scientists across nine projects, 17 countries and 65 research institutions, and Schmidt Sciences has granted 26 million dollars to researchers working on translating climate models into climate action, so this challenge extends an established portfolio. Applicants should expect to compete against teams with existing coupled-model infrastructure, and a proposal without access to a working coupled model to improve is at a structural disadvantage.
Schmidt Sciences opened a 'Scaling AI Safety for a Multi-Agent World' program with awards up to $1 million and an August 8, 2026 deadline. Against a backdrop of federal research slowdowns, here is why private science philanthropy matters more than ever, how these funders differ from federal agencies, and how researchers should approach them.
Read articleSchmidt Sciences' 2026 Science of Trustworthy AI RFP closes May 17 with two funding tiers — up to $1M (Tier 1) and $1–5M+ (Tier 2) over 1–3 years, with a 10% indirect cost cap. The three research aims target misalignment under distribution shift, predictive-validity evaluations, and oversight of superhuman systems. Here is why the structure favors team-based proposals.
Read articleThe U.S. Government Policy for Stopping High-Risk Life Sciences Research, released July 28, 2026 under EO 14292 and implemented at NIH through NOT-OD-26-101, replaces mitigation with prohibition. It bans dangerous gain-of-function research outright, gates potential DGOF behind government-wide review, and creates International Research of Concern — a research-security regime wearing biosafety clothing. Penalties run to five-year debarment and False Claims Act exposure.
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