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Transformative Sustainable AI Technologies is the sustainability arm of EPSRC's Next Generation Artificial Intelligence programme, funding speculative and high-risk research on making AI systems sustainable, which the call defines as developing systems that maximise long-term economic and societal value while minimising environmental harm.
The total fund is 12,050,000 pounds sterling, the maximum award is 602,500 pounds at full economic cost funded at 80 per cent by UKRI, the opportunity opened on 24 August 2026 and closes on 10 November 2026 at 4pm UK time, projects start on a fixed date of 1 February 2027 and run for up to 24 months.
Priority areas are energy-efficient algorithms and system architecture, smaller task-specific models as an alternative to scaling, optimisation of AI for resource-constrained environments, hardware-software co-design, and repairable and recyclable hardware.
The call draws a firm scope boundary: applications must be technology-led and cannot consist predominantly of social science, environmental, legal, policy or ethical study, which distinguishes it from the large body of AI-and-climate funding that supports governance and assessment work.
Assessment is by distributed peer review with applicants reviewing peer proposals and up to eight reviews informing each decision, and the criteria are vision and approach, applicant and team capability, ethics and responsible research and innovation, resources and cost justification, and risk and reward. Research technical professionals including research software engineers are named as eligible team members.
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Or search similar grants →According to the current listing, eligibility includes: Eligibility is restricted to applicants based at UK research organisations eligible for UKRI funding who meet UKRI individual eligibility requirements, covering UK universities, approved research institutes and recognised independent research organisations. The call explicitly welcomes research groups and individuals, research technical professionals including research software engineers, and researcher co-leads who make a substantial intellectual contribution. International researchers may participate only as project co-leads and only through specific cooperation agreements, the UKRI-RCN Money Follows Cooperation route or the UKRI-IIASA agreement, so overseas partners cannot be funded as ordinary consortium members and cannot lead. A participation cap applies: no more than two applications per person to this opportunity, of which only one may be as project lead. The most consequential eligibility constraint is thematic rather than institutional. The opportunity requires a technology-led proposal and rules out applications that consist predominantly of social science, environmental, legal, policy or ethical studies, so a project framed around measuring or governing the carbon footprint of AI rather than changing the technology that produces it will be rejected on scope. Applications go through the UKRI Funding Service, not the legacy system, and require a Vision and Approach document of up to four pages together with capability, ethics, resources and risk sections. Submitting carries a reciprocal obligation to participate in distributed peer review of other applications in the cohort. The deadline is 10 November 2026 at 4pm UK time and the 1 February 2027 project start is fixed. Confirm the full requirements in the official notice before applying.
The current listing shows the total fund is 12,050,000 pounds sterling with a maximum award of 602,500 pounds sterling at full economic cost, approximately 801,000 US dollars, and amount_min and amount_max are both recorded as 801,000 because only a ceiling is published. UKRI funds 80 per cent of the full economic cost, so a project costed at the ceiling yields roughly 482,000 pounds, about 641,000 US dollars, in UKRI cash with the host institution contributing the remaining fifth. The ceiling is expressed in FEC terms and applicants who cost to 602,500 pounds of expected cash rather than 602,500 pounds of FEC will be over the limit. The pot divided by the ceiling implies in the region of twenty awards, and the fixed 1 February 2027 start with a maximum 24-month duration means the ceiling supports roughly two years of a small team. Budgeting for this particular call has a wrinkle that the parallel explainability call does not: sustainability research frequently requires measurement infrastructure, energy instrumentation of compute, access to varied hardware, or fabrication of repairable and recyclable components, and these are capital-flavoured costs that sit awkwardly inside a 24-month FEC envelope. Applicants proposing hardware-software co-design or recyclable hardware work should confirm what equipment costs are admissible under the opportunity's resources section before sizing the request, and should note that the call is explicitly technology-led, so budget weighted towards social science, policy or legal analysis will read as out of scope regardless of total size. Verify award ceilings, matching requirements, and allowable costs in the official notice.
Applications for UKRI EPSRC Transformative Sustainable AI Technologies for Energy-Efficient Algorithms, Small Task-Specific Models and Hardware-Software Co-Design are due November 10, 2026. Build your timeline backwards from this date to cover registrations, approvals, and final submission checks.
UKRI EPSRC Transformative Sustainable AI Technologies for Energy-Efficient Algorithms, Small Task-Specific Models and Hardware-Software Co-Design is funded by UK Research and Innovation (UKRI), Engineering and Physical Sciences Research Council (EPSRC). 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.
HORIZON-RAISE-2026-01-03 is the doctoral training instrument of the European Commission's RAISE pilot, the Resource for AI Science in Europe, and it funds doctoral networks dedicated to the development and application of artificial intelligence in science. The indicative budget is 30,000,000 euros across three sub-areas, standard doctoral networks, industrial doctorates and joint doctorates, the type of action is HORIZON TMA MSCA Doctoral Networks, the topic opened on 28 May 2026 and the deadline is 24 November 2026. The topic builds directly on the Marie Sklodowska-Curie Doctoral Networks 2026 call and effectively adds a dedicated AI-in-science channel to it, which matters because it means the proposal format, unit-cost funding model, mobility rules and evaluation criteria are the familiar MSCA ones rather than a new RAISE-specific framework. Expected outcomes are new research skills and transferable competences in applying AI in science with improved employability inside and outside academia, new knowledge that can be converted into products and services, and strengthened networking and communication capacity. This sits alongside the RAISE thematic networks of excellence topics and the automated scientific discovery topics as the human-capital pillar of a roughly 90,000,000 euro AI in Science package in the Horizon Europe 2026-2027 work programme, and it is the only part of that package aimed squarely at training rather than at research output.
UKRI's Enabling AI Adoption Across Science and Engineering opportunity invites ambitious and exploratory projects that bring AI expertise together with domain expertise to attack domain-specific research challenges. EPSRC is the lead funder, the total fund is 5,000,000 pounds sterling, the maximum award is 250,000 pounds at full economic cost funded at 80 per cent, the call opened on 29 September 2026 and closes on 1 December 2026 at 4pm UK time, and funded projects start on a fixed date of 26 April 2027 with a maximum duration of twelve months. Proposals must address a challenge within one of five named priority areas: engineering biology, advanced materials, quantum technologies, medical research, and fusion energy. The defining structural requirement is that teams must include both AI specialists and non-AI domain experts, making this a brokered-collaboration instrument rather than a conventional AI methods call; the money is intended to move AI capability into domains that have not yet absorbed it rather than to advance AI as a field. A webinar for potential applicants was held on 14 October 2026. Assessment uses UKRI's distributed peer review model, under which at least two members of each applicant team must review proposals from other applicants, and applications are submitted through the UKRI Funding Service with a Vision and Approach document of up to four pages plus capability, ethics, resources and risk sections.
HORIZON-RAISE-2027-01-02 is the food-sector topic of the European Commission's automated scientific discovery strand within the RAISE pilot, the Resource for AI Science in Europe. It funds a single Research and Innovation Action, with an indicative budget of 3,000,000 euros, aimed at safe and trustworthy closed-loop scientific experimentation in food science through the integration of laboratory automation, AI-driven decision-making and robust data infrastructure. The topic opened on 22 September 2026 with a deadline of 2 February 2027. It is the smaller companion to HORIZON-RAISE-2027-01-01, which carries 30,000,000 euros for three projects on automated scientific discovery in general, and the pairing reflects the Commission's pattern throughout the AI in Science package of issuing a broad topic alongside a sector-specific one, as it did with the 2026 networks of excellence topics for materials and for agriculture and environmental pollution. Automated scientific discovery, sometimes described as self-driving laboratories, is the coupling of robotic experimentation with AI models that select the next experiment, and applying it to food means working across formulation, processing, safety, nutrition and sensory evaluation where experimental cycles are slow and data are fragmented. The emphasis on safety and trustworthiness in the topic text signals that validation, traceability and data governance will carry real evaluation weight alongside autonomy and throughput.
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