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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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Or search similar grants →According to the current listing, eligibility includes: Standard Horizon Europe collaborative eligibility applies: the consortium must include at least three independent legal entities from three different countries, with at least one established in an EU Member State and the remainder in Member States or Horizon Europe Associated Countries, which includes the United Kingdom, Switzerland, Norway and other associated states. Eligible participant types are universities, research and technology organisations, companies of all sizes including SMEs, public bodies and non-profit organisations, and entities from certain low and middle income third countries may be funded under the general Horizon Europe rules. The decisive constraints are thematic. The project must deliver closed-loop scientific experimentation, meaning the integration of laboratory automation with AI-driven selection of subsequent experiments plus the data infrastructure to support it, and it must do so in the food domain; a proposal that applies machine learning to existing food datasets without the automated experimental loop does not meet the topic, and a strong automated-discovery proposal in another domain belongs under HORIZON-RAISE-2027-01-01 instead. Because the indicative budget funds a single project of 3,000,000 euros, consortia should be small and should bring existing automated laboratory capability rather than propose to build it. Research and Innovation Actions fund 100 per cent of eligible direct costs with a 25 per cent indirect cost flat rate and no cost sharing. The topic opened on 22 September 2026, the deadline is 2 February 2027, and submission is through the EU Funding and Tenders Portal. Confirm the full requirements in the official notice before applying.
The current listing shows the topic has an indicative budget of 3,000,000 euros, approximately 3,510,000 US dollars, for a single project, so amount_min and amount_max are both recorded as 3,510,000 and the total call budget and the individual award are the same figure. Horizon Europe Research and Innovation Actions reimburse 100 per cent of eligible direct costs plus a 25 per cent flat-rate indirect cost contribution, so there is no cost-share burden and the practical meaning of a 3,510,000 US dollar envelope is roughly three to four years of a small consortium rather than a large infrastructure build. This is by a wide margin the smallest topic in the RAISE family, sitting against 30,000,000 euros for the sister automated scientific discovery topic HORIZON-RAISE-2027-01-01 and 15,000,000 euros for the materials network of excellence, and the gap is the single most important budgeting fact about it. A single-project 3,000,000 euro topic cannot fund construction of a new self-driving laboratory for food science from scratch, so a credible budget leverages existing automated experimentation facilities, existing food-science datasets and existing model development capability within the consortium and spends the grant on integration, the AI decision-making layer and validation. Consortia that attempt to spread 3,000,000 euros across a large partnership will find the per-partner allocation too thin to deliver closed-loop autonomy, which argues for a tight consortium close to the three-entity minimum. Verify award ceilings, matching requirements, and allowable costs in the official notice.
Applications for Horizon Europe HORIZON-RAISE-2027-01-02 Automated Scientific Discovery for Food for AI-Driven Closed-Loop Experimentation in Food Science are due February 2, 2027. Build your timeline backwards from this date to cover registrations, approvals, and final submission checks.
Horizon Europe HORIZON-RAISE-2027-01-02 Automated Scientific Discovery for Food for AI-Driven Closed-Loop Experimentation in Food Science is funded by European Commission, Horizon Europe Horizontal Activities, Resource for AI Science in Europe (RAISE) pilot. 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.
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.
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.
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