Neutral-Atom Platform Technologies
This action aims to establish European leadership in neutral-atom quantum simulation and digital quantum computing by delivering: • Fully programmable industry-ready quan…
Horizon Europe · European Union
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The EU's Horizon Europe programme expects to make 3 awards of €10 million each, from a total budget of €30 million. Applications close on 1 Dec 2026 (EU calls usually close at 17:00 Brussels time). Organisations in EU member states and Horizon Europe associated countries such as Türkiye can usually take part; the topic conditions control.
Projects are expected to contribute to all the following expected outcomes:
• Effective and innovative methods for gathering, sharing and using large data sets in energy applications for the purpose of training AI models while ensuring privacy and security.
• Advanced and - wherever possible - open-source AI foundation models to support the digitalisation of the energy system, through improved grid observability, forecasting of supply and demand, advanced storage and renewables integration, demand side flexibility and energy efficiency.
• Enhanced cooperation, knowledge sharing and interoperability among energy system actors for secure and seamless data exchange.
• Advanced methodologies for AI model development ensuring that the results are FAIR (Findable, Accessible, Interoperable, Reusable) beyond the project ending, open source and accommodating for technology evolution.
The quality of AI models depends strongly on the amount, quality, and representativeness of the data used for their training. Projects are expected to focus on developing, training and testing AI foundation models, using extensive data sets to accelerate the energy transition in key focus areas of the energy sector. Projects are expected to:
• Building on the results of previously funded projects on the Common European energy data space [1] as well as the ongoing work within the Data 4 Energy expert group, projects are expected to demonstrate innovative methods and data governance strategies for sharing data among various energy actors. Projects are expected to demonstrate that they have access to large relevant datasets at the time of submitting the proposal.
• Projects are expected to develop innovative strategies for sharing data in an effective, secure and transparent way to support the development of AI foundation models. An indicative non exhaustive list of strategies that can be explored is the use of data space connectors or federated training of models to allow the integration of confidential data; the creation of synthetic data as an intermediate step to fill data gaps or improve the quality of the data could also be explored. Projects could also explore other innovative data sharing strategies, such as an ‘AI gym’. The data governance strategies that will be developed in the project should be designed in a way to be scalable and operational even after the end of the project.
• Projects are expected to develop foundation models tailored for the energy sector, addressing at least one -or more- of the following use cases: planning and operation of electricity grids (including static power flow modelling and dynamic EMT modelling), forecasting, congestion management, anomaly detection, fault diagnosis, predictive maintenance, flexibility management, demand-side energy efficiency, smart and bidirectional charging of EVs or other use cases that contribute to the objective of digitalisation of the energy system.
• Projects are expected to perform in-depth testing and validation of developed foundation models in lab demonstrators and real-life pilots, building on the results and using the facilities of previously funded projects on “AI testing and experimentation facilities (TEFs)” [2] . Regulatory sandboxes could also be considered for real-life pilot implementations.
• Projects are expected to bring together a wide group of stakeholders including data owners (for example energy utilities, grid operators, asset owners), application developers (for example startups, SMEs, hyperscalers) and model deployers (for example industry, grid operators, equipment manufacturers) providing a space of cooperation and collaborative data exchange.
• To scale up the training and development of the models, projects can benefit from the computing capacity of the AI factories that were recently announced by the European Commission [3] , in particular the three…
Consortium of at least 3 independent legal entities from 3 different EU Member States or associated countries, at least one in a Member State (Horizon Europe standard rule).
Applicant categories are a starting point. The official call decides eligibility.
Single-stage call. Next deadline 2026-12-01. The portal states the exact cut-off time (EU calls usually close at 17:00 Brussels time).
| Fact | Value | How we know | Taken from the source |
|---|---|---|---|
| Deadline | 1 December 2026 “Single-stage call. Next deadline 2026-12-01. The portal states the exact cut-off time (EU calls usually close at 17:00 Brussels time).” | official data field | 15 Nov 2026 |
| Amount per applicant | Unknown — not stated in the source we hold | unknown | — |
| Total budget | €30 million | official data field | 15 Nov 2026 |
| Funding rate | 70% for companies, 100% for non-profits | programme standard rule | 15 Nov 2026 |
| Who can apply | Consortium of at least 3 independent legal entities from 3 different EU Member States or associated countries, at least one in a Member State (Horizon Europe standard rule). | programme standard rule | 15 Nov 2026 |
| Where to apply | Read the topic conditions on the EU Funding & Tenders Portal for eligible countries, consortium requirements, budget and the application route. | read from the official text | 15 Nov 2026 |
Facts on this page come from the funder’s official data, taken on 14 Nov 2026. The funder's own documents, including later amendments, always take precedence.
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