| Topic | HORIZON-BRIDGING-2027-01-13 |
|---|---|
| Status | Forthcoming |
| Call | HORIZON-BRIDGING-2027-01 — Bridging actions 2027 |
| Type of action | Research and Innovation Actions |
| Opening | 20 October 2026 |
| Deadline | 16 February 2027 |
| Budget | around EUR 15 million per project · 1 project expected · EUR 15 million in total |
| TRL at the end | TRL 4–5 |
| Part B page limit | 40 pages (standard application form; check your call) |
| Template | Work Programme 2026–2027 application form |
Expected outcomes
Projects may contribute to some of the expected outcomes; say which ones and why. The topic says: “This topic aims to advance the scientific and technical foundations for the scaling of AI models, enhancing the performance of training and inference processes and providing European AI labs with the methodologies required to compete at the global frontier. Project results are expected to contribute to some of the following outcomes:”
- EO1 Establishment of robust scaling laws and predictive frameworks that allow for the reliable estimation of model performance before large-scale compute investment.
- EO2 Enhanced competitiveness of European advanced AI models through optimised dataset mixtures, refined architectures, and superior hyperparameter configurations.
- EO3 Optimised development for frontier AI models, achieved via automated and scientifically grounded scaling methodologies.
What evaluators will look for in this topic
Expected outcomes
- Projects may contribute to some of the expected outcomes; say which ones and why.
- For each outcome (EO1 to EO3), name who will use or adopt your results (a specific group, not “industry” or “society”), how they get them, and the indicator that shows the change, with baseline, target and date.
- Use the topic's own words for each outcome, so the evaluator can tick it off.
What the scope asks for
Evaluators check the proposal against these sentences of the scope. Answer each one visibly, in the call's own words.
- “To maintain a leading position in AI, Europe must master the methodologies required to scale AI models’ performance, training and inference processes.”
- “In this light, proposals should deliver, as an outcome of the research, a toolset designed to support the scaling of AI models.”
- “Proposals should ensure that the developed frameworks and scaling methodologies are compatible with diverse hardware architectures, demonstrating that scaling is studied across different systems to avoid optimisation for specific proprietary hardware.”
- “The selected project should actively support Advanced AI labs and High-performance Computing Centres already engaged in the development of frontier AI, ensuring that methodological advances are immediately validated in high-stakes environments.”
- “Proposals should describe how these scaling methodologies would benefit the development of models adapted to the specific requirements of particular strategic sectors, such as Public Administration, Manufacturing, Energy, Healthcare, Cultural and Creative Industries, or Physical AI.”
- “Proposals should, therefore, describe the link between their planned activities and the objectives of topic HORIZON-CL4-2027-04-DIGITAL-EMERGING-11.”
- “All proposals are expected to incorporate mechanisms for assessing and demonstrating progress, including qualitative and quantitative KPIs, benchmarking, and progress monitoring.”
- “When possible, proposals should build on and reuse public results from relevant previous funded actions.”
- “Communicable results should be shared with the European R&D community through the AI-on-demand platform.”
- “The project selected in this topic should link to the resources offered by the AI Factories and the Data Labs.”
- “All proposals are expected to allocate tasks for cohesion activities with the European Partnership on AI, data, and robotics (ADRA) and the CSA HORIZON-CL4-2025-03-HUMAN-18: GenAI4EU central Hub.”
Conditions that shape the proposal
- Part B, sections 1–3: 40 pages (standard application form). Text beyond the limit is not read; tables and figures count.
- TRL: the topic expects TRL 4–5 by the end of the project. State where you start and the evidence for it.
- Other Eligibility Conditions: “In order to achieve the expected outcomes, and safeguard the Union’s strategic assets, interests, autonomy, and security, it is important to avoid a situation of technological dependency on a non-EU source, in a global context that requires the EU to take action to build on its strengths, and to carefully assess and…”
- Other Eligibility Conditions: “For the duly justified and exceptional reasons listed in the paragraph above, in order to guarantee the protection of the strategic interests of the Union and its Member States, entities established in an eligible country listed above, but which are directly or indirectly controlled by a non-eligible country or by a…”
- Other Eligibility Conditions: “[[ The guarantees shall in particular substantiate that, for the purpose of the action, measures are in place to ensure that: a) control over the applicant legal entity is not exercised in a manner that retrains or restricts its ability to carry out the action and to deliver results, that imposes restrictions…”
Impact pathway and traps
- The scope expects pilots or demonstration. Name the sites, the partners who run them and what is measured there.
- Keep outputs, outcomes and impacts apart: what you deliver, what changes when others use it, and the long-term effect.
- Link your outcomes to the destination's expected impacts in the Work Programme. Destination impacts are long-term effects, not project deliverables.
- Name the barriers outside the project (regulation, markets, user behaviour) that could block uptake, and what you will do if they do.
- Section 2.3 (the summary canvas) is optional on this call; the old split into scientific, economic and societal impact is no longer required.
The scope, as published
To maintain a leading position in AI, Europe must master the methodologies required to scale AI models’ performance, training and inference processes. Establishing a predictive methodology for AI model development ensures that limited computational resources are deployed with maximum efficiency, thereby bolstering the EU and AC’s technological sovereignty. While previous actions have focused on training High-Performance AI models, this topic addresses the research gap in the underlying theoretical basis, methodologies and tools for predictable scaling of AI models and inference. By providing the technical foundations for high-performance systems without the costs of unguided experimentation, this action complements training-centric efforts. In this light, proposals should deliver, as an outcome of the research, a toolset designed to support the scaling of AI models. Potential research areas include, but are not limited to: Automated Scaling Law Derivation: Research into scaling laws to predict performance across diverse modalities and tasks, moving beyond simple power laws. Exploratory Architecture Search: Developing automated methods to discover and refine model architectures that maintain performance while scaling both in training and inference, including innovations in sparsity, memory efficiency, and long-context handling. Dataset Mixture Optimisation: Research into the systematic selection, weighting, and sequencing of datasets. This includes developing tools for automated data quality assessment. Advanced Hyperparameter Tuning and Stability: Developing methodologies for stable training at large scales, including automated tuning of optimisers and learning rate schedules that remain effective as models grow. Proposals should ensure that the developed frameworks and scaling methodologies are compatible with diverse hardware architectures, demonstrating that scaling is studied across different systems to avoid optimisation for specific proprietary hardware. The selected project should actively support Advanced AI labs and High-performance Computing Centres already engaged in the development of frontier AI, ensuring that methodological advances are immediately validated in high-stakes environments. Proposals should describe how these scaling methodologies would benefit the development of models adapted to the specific requirements of particular strategic sectors, such as Public Administration, Manufacturing, Energy, Healthcare, Cultural and Creative Industries, or Physical AI. Part of the Frontier AI Initiative, this topic complements HORIZON-CL4-2027-04-DIGITAL-EMERGING-11 “EU Frontier AI Initiative: Developing frontier AI solutions that are safe and computationally efficient within Apply AI” by establishing the underlying methodologies required for the large-scale training and inference of High-Performance AI models. Proposals should, therefore, describe the link between their planned activities and the objectives of topic HORIZON-CL4-2027-04-DIGITAL-EMERGING-11. All proposals are expected to incorporate mechanisms for assessing and demonstrating progress, including qualitative and quantitative KPIs, benchmarking, and progress monitoring. When possible, proposals should build on and reuse public results from relevant previous funded actions. Communicable results should be shared with the European R&D community through the AI-on-demand platform. The project selected in this topic should link to the resources offered by the AI Factories and the Data Labs. Where relevant, links could be established to European companies developing frontier AI models to foster a pre-competitive exchange of expertise and explore ways to mobilise the wider developer community for the efficient reuse of results. All proposals are expected to allocate tasks for cohesion activities with the European Partnership on AI, data, and robotics (ADRA) and the CSA HORIZON-CL4-2025-03-HUMAN-18: GenAI4EU central Hub.
Technology readiness, as published
Activities are expected to achieve TRL 4-5 by the end of the project – see General Annex B.
Other topics in HORIZON-BRIDGING-2027-01
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- HORIZON-BRIDGING-2027-01-10 — Autonomous Drive Ambition Cities (IA) - Apply AI
- HORIZON-BRIDGING-2027-01-12 — Large-Scale piloting of next-generation AI-driven digital platforms for a more…
Source and attribution
Topic text from the official topic page on the EU Funding & Tenders Portal, retrieved 19 October 2026. © European Union, reused under Commission Decision 2011/833/EU (CC BY 4.0). Changes: the expected outcomes and impacts are split into numbered statements, long passages are shortened, and the evaluator checklist is GrantWise's own commentary. Your call and the Work Programme always win; check them before you submit. GrantWise is independent and not affiliated with, endorsed by, or acting on behalf of the European Commission.