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The US-China AI Governance PhD Fellowship funds doctoral students working on risk reduction in the US-China relationship as it bears on artificial intelligence - a research area with acute policy relevance and almost no dedicated funding stream. The award covers tuition and fees for up to five years, a 40,000 dollar annual stipend at US, UK and Canadian universities, and a 10,000 dollar annual research fund.
The most recent deadline was 21 November 2025, and FLI has announced it will not run a Fellowships round in autumn 2026 while it reassesses its fellowship programs, so the next window is uncertain.
Six research directions are named: global governance approaches that mitigate AI race dynamics and the safety problems they create; the political factors that determine whether US-China cooperation is effective; the technology characteristics that make bilateral engagement more or less feasible; the actual current extent of collaboration and how cooperative institutions should be designed; how each nation understands and manages AI risk and where those approaches diverge; and the role third countries can play in reducing competition-driven risk.
This is a governance and international-relations call rather than a technical one, and applicants from political science, law, security studies and economics are the intended audience. The framing worth noting is that FLI is funding scholarship on race dynamics specifically - proposals that treat US-China AI competition as a subject of description rather than as a risk to be reduced are unlikely to match the brief.
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Or search similar grants →According to the current listing, eligibility includes: Open to PhD students who plan to focus their doctoral research on US-China AI governance, in either of two categories: students applying to or entering a PhD program with that intended focus, and current PhD students who lack alternative funding for that research area. The published stipend applies at universities in the United States, United Kingdom and Canada. Shortlisted candidates receive application fee reimbursement for up to five programs. The award comprises tuition and fees for up to five years of PhD study, a 40,000 dollar annual stipend and a 10,000 dollar annual research fund. Eligible research spans global governance mechanisms for mitigating AI race dynamics, political determinants of effective US-China cooperation, technology characteristics affecting the feasibility of bilateral engagement, the current extent of collaboration and institutional design for cooperation, comparative analysis of how each country manages AI risk, and the role of third countries in reducing competition-related risk. Applicants should expect to name a supervisor capable of overseeing work at the intersection of AI policy and international relations, which is the practical bottleneck for this scheme. The most recent deadline was 21 November 2025 and no autumn 2026 round will be held; contact grants@futureoflife.org to confirm the status of future cycles. Confirm the full requirements in the official notice before applying.
The current listing shows the fellowship pays a 40,000 dollar annual stipend at universities in the United States, United Kingdom and Canada plus a 10,000 dollar annual research fund, and covers tuition and fees for up to five years of PhD study. amount_min of 50,000 is the annual cash package and amount_max of 250,000 is that package across the full five-year term, with tuition and fees additional to both. Shortlisted candidates receive application fee reimbursement for up to five programs. What makes this distinct from FLI's technical fellowship is that the funded object is a social-science research agenda, not an ML one, and the stipend is therefore competing against political science and international relations departmental funding rather than against a frontier-lab salary - which makes 50,000 dollars a year a genuinely strong offer in that market rather than a merely adequate one. The scarcity here is not money but supervisors: very few departments house anyone equipped to supervise US-China AI governance work, and identifying a credible advisor is the real constraint on a competitive application. Verify award ceilings, matching requirements, and allowable costs in the official notice.
The published deadline was November 22, 2025, which has passed. Check the official notice for any future application windows before investing time in a proposal.
Future of Life Institute US-China AI Governance PhD Fellowship is funded by Future of Life Institute. 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.
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.
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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