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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.
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Or search similar grants →According to the current listing, eligibility includes: Open to research teams, principally at universities and research institutions, that can implement and test new methods for coupled climate model calibration and model updating within a 24-month period. Applications were submitted as expressions of intent by 11 September 2026, with shortlisted teams invited to a full proposal stage; the process is two-stage, so the initial submission burden is comparatively low and teams uncertain about fit should submit rather than self-select out. Up to five teams will be funded at up to 2 million US dollars each. In-scope approaches include machine learning methods, improved physical process representations and advanced calibration workflows, and proposals are judged on whether they make climate models faster to improve, more reproducible and more accurate rather than on methodological novelty alone. Practical prerequisites are access to a coupled climate model and the computational infrastructure to test methods at realistic scale; teams without an existing modelling platform should consider partnering with one of the institutions already in the VESRI network. Schmidt Sciences operates internationally and the existing VESRI portfolio spans 17 countries, so non-US applicants are eligible. Applicants should verify current-round status and any successor call directly at schmidtsciences.org/vesri, since Schmidt Sciences runs VESRI calls episodically rather than on a fixed annual cycle. Confirm the full requirements in the official notice before applying.
The current listing shows awards are up to 2,000,000 US dollars per team over 24 months, and the challenge expects to fund up to five teams, so amount_min and amount_max are both 2,000,000 against a published ceiling with no stated floor. Five awards at the ceiling implies a programme pool of roughly 10 million dollars. The 24-month clock is the constraint that should shape scoping: this is not an open-ended modelling grant but a challenge to demonstrate that a specific calibration or model-update method actually shortens development cycles, and the deliverable is implemented and tested methods rather than a paper. Schmidt Sciences has previously granted 26 million dollars through VESRI to researchers translating climate models into climate action and currently coordinates nine projects across 17 countries and 65 research institutions, so a successful team is joining an existing network rather than starting a standalone effort. Machine learning is named as one of the in-scope approaches alongside improved physical process representations and advanced calibration workflows, which means a pure-ML proposal competes against physics-based submissions on the same criterion: measurable acceleration of the model development cycle. Verify award ceilings, matching requirements, and allowable costs in the official notice.
The published deadline was September 11, 2026, which has passed. Check the official notice for any future application windows before investing time in a proposal.
Schmidt Sciences VESRI Climate Modeling Challenge: Accelerating Development Cycles for Coupled Climate Models is funded by Schmidt Sciences, Virtual Earth System Research Institute (VESRI). Verify program details on the funder's official page before applying.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
CIFAR and the Canadian AI Safety Institute fund Catalyst Project proposals addressing sociotechnical considerations in AI safety. The program supports interdisciplinary research in machine learning applications to science and society, with recent funded projects spanning misinformation combat, trustworthy language models, democratic alignment of AI systems, Indigenous AI governance, and real-world safety in autonomous systems. Designed to catalyze new research areas and collaborations at the intersection of social sciences, humanities, and AI safety.
Lightcone Commons is a grantmaking platform operated by Lightcone Infrastructure that coordinates large-scale philanthropy in quarterly rounds, with a first round deadline of 23 August 2026 at 11:59pm Anywhere on Earth and grant recommendations announced around 23 October 2026. It expects to distribute 15 to 25 million US dollars in the first round and comparable amounts every three months thereafter, with rolling applications between rounds. The platform's design solves a specific problem in AI safety funding: rather than applying separately to each funder, an applicant submits once and is seen by multiple funders operating from different worldviews. Significant participating funders emphasise AI alignment. The Long Term Future Fund and the AI Risk Mitigation Fund each allocate roughly 2 million dollars and focus almost exclusively on shaping AI development in a positive direction, while Jaan Tallinn, the largest individual participant, directs the bulk of his funding toward work aimed at aligning, controlling or otherwise shaping superintelligent AI systems, with some giving extending to other ambitious causes. Eligibility is unusually broad: applicants need not be 501(c)(3) organisations, and the platform can coordinate funding for for-profits, individuals, educational institutions, 501(c)(4)s and non-US entities. Practical obstacles exist for applicants based in certain countries including Russia and India. The tradeoff to understand is that per-grant sizes are not published and funding decisions rest with individual funders, so the platform offers reach rather than a predictable award.
The AI Safety Policy Entrepreneurship Fellowship is FAS's route for turning technical and domain expertise into frontier AI safety policy that actually moves. It is a part-time hybrid programme running 30 September 2026 to 28 February 2027, with applications due 7 September 2026, and it pays a USD 5,000 stipend plus up to USD 1,000 as a merit award. Fellows commit roughly five hours a week to developing a policy memo on a specific AI safety challenge, attend training sessions, join an in-person retreat in California from 4 to 7 November 2026, and present at a capstone event in Washington DC during the week of 22 February 2027. The design target is explicit and unusual: it recruits early- to mid-career professionals who have limited direct public policy experience but deep expertise elsewhere - technical AI research, academia, think tanks, civil society, industry, law, cybersecurity and national security - and teaches them the mechanics of getting an idea adopted. Selection weighs clear understanding of AI governance challenges, concrete implementation-oriented solutions rather than broad principles, awareness of which stakeholders must be moved, and a credible commitment to translating expertise into policy outcomes. Because the stipend is modest and the time commitment part-time, this is designed to sit alongside an existing job rather than replace one.
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