Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Anion Exchange Membrane Water stack based on Earth Abundant 2D Materials for Green Hydrogen Production (2D4H2)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Countries involved: Spain
Call: ERC PROOF OF CONCEPT GRANTS2 (ERC-2022-POC2, 2022)
Start date: 2023-03-01
End date: 2024-08-31
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: CLOSED

The baseline technology for green H2 production is the water-electrolysis (WE). However, roughly 96% of the H2 produced today is from fossil fuels, with the remaining 4% produced through water electrolysis , due to the still-high costs, and lower performance of current electrolysers compared with other production processes not affected by the use of toxic or critical raw materials (CRM). Therefore, there is a need of boosting the development of highly active and efficient catalysts to turn green hydrogen into a viable solution to decarbonise different sectors and to meet the ambitious goals settled in the Hydrogen Strategy. This project proposes an advanced Anion Exchange Membrane Water Electrolyser (AEMWE) stack as a critical milestone to translate the highly promising results coming from the ground-breaking research conducted during the ERC-StG awarded to Dr G. Abellán, into a marketable innovation. The AEMWE stack novelty relies on non-toxic CRM-free breakthrough novel electrodes (anodes) made of two-dimensional (2D) nickel-iron layered double hydroxide materials (2D NiFe-LDHs) that have shown an outstanding catalytic behaviour. Using this electrocatalytic material will allow overcoming the main challenges of WE to produce green H2. The activities to be undertaken under the 2D4H2 project, are aimed to prepare the translation of the 2D NiFe-LDHs electrocatalytic materials into an AEMWE stack as a precursor of a future fully operational 0.5kW electrolyser. For this purpose, the necessary optimisation and characterisation of the electrocatalyst to enable the testing and validation in a pilot plant of the single unit cell, and the AEMWE stack prototype, will be carried out together with the elaboration of an integrated strategy for effectively managing the knowledge generated during the project, including clarifying the IPR position, and an exploitation strategy involving potential stakeholders in order to evolve the idea further towards exploitation.
Cutting-Edge AI Solution for Engineering, Procurement, and Construction (EPC) and Underground Utilities Mapping (DeepGeo.AI)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Accelerator
Destination: Other destinations
Action type: EIC actions
Countries involved: Italy
Call: EIC Accelerator 2025 - Short application (HORIZON-EIC-2025-ACCELERATOR-01, 2025)
Duration (months): 18.0
Total project cost (EUR): 2500000.0
EU funding (EUR): 2500000.0
Participants: 1
Project status: CLOSED

Across Europe, critical infrastructure projects are crippled by a fundamental challenge: the limited and fragmented knowledge of the subsurface. With about 48% of utilities not documented on underground maps or in outdated records, every excavation poses a significant risk, resulting in hundreds of thousands of utility strikes per year in Europe. These lead to unacceptable safety incidents in the civil engineering and energy sectors, major project delays, and budget overruns with a total economic and social cost of about €50 billion annually. Traditional subsurface mapping methods are slow, invasive, and heavily reliant on subjective human interpretation, resulting in inconsistent data and a false-positive rate exceeding 90%. This inefficiency leads to weeks of costly rework and critical safety risks. Xplora is solving this uncertainty with DeepGeo.AI, a breakthrough, next-generation AI platform. Our innovation delivers real-time, 3D, georeferenced digital twins of the subsurface down to 14 meters. DeepGeo.AI fuses data from radar, magnetometers, and electromagnetic sensors, leveraging advanced AI to transform millions of data points into actionable insights in mere hours, eliminating subjective interpretation. With DeepGeo.AI, we scan 1 hectare per hour, reduce project costs by 70%, and achieve a field-validated false-positive rate below 1%. DeepGeo.AI is currently at TRL 7, having been successfully deployed in complex, real-world conditions across Europe. These include mapping pipelines in dense urban areas, identifying unexploded ordnance (UXO), and supporting smart grid development. The EIC Accelerator project will accelerate the completion of development and help us enter our growth phase. Our goals are to reach TRL 8 by Q2 2028, achieve breakeven, and generate over 70 high-value European jobs in AI and geospatial technology by 2029. With the EIC’s support, we will empower Europe to build better, faster, and smarter — from the ground up.

Keywords: subsurface mapping,geophysical sensing,utility strikes,sensor fusion,ai algorithms,digital twin,smart infrastructure,underground utilities,non invasive surveying,energy infrastructure
Membrane Micro-Compartments (MMC)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Countries involved: Germany
Call: ERC PROOF OF CONCEPT GRANTS (ERC-2023-POC, 2023)
Start date: 2024-02-01
End date: 2025-07-31
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: SIGNED

More than two-thirds of all described drug targets are membrane proteins, but only for a small fraction of these hydrophobic proteins the structure is currently known. Membrane proteins are inherently challenging to produce and analyze. Consequently, in pre-clinical investigations their mechanism and drug interactions are often only derived from purified truncated parts of these proteins, taken out of their native biological context. Lacking the effects of surrounding membrane components, conclusions on drug mechanisms are indecisive. Recent advances in in situ structure determination techniques, i.e. analysis directly within the cellular environment, now have the power to overcome the limitations of classical reconstituted approaches. Complementary, we have developed a system that may facilitate structural analysis of membrane protein drug targets and has large potential for the production of native membrane proteins - an essential prerequisite for the study of membrane protein function and drug interactions. Within the scope of our proposal, we will further develop our system and transfer it into pharmaceutically relevant cell systems. Furthermore, we aim to achieve proof-of-concept and assess our system with clinically relevant membrane proteins for their suitability to solve 3D structures in complex with clinical drugs, as well as its suitability for the membrane protein production. We are convinced that our approach will have a transformative impact on how drug - membrane protein interactions can be studied, and could be exploited for drug development in the pharmaceutical industry. In the course of the project, we will establish a plan for transforming our system into a business model and commercializing our technology.
TRAnsparent InterpretabLe robots (TRAIL)

Framework: Horizon Europe — Excellent Science
Programme: Marie Skłodowska-Curie Actions (MSCA)
Theme: Marie Skłodowska-Curie
Destination: Doctoral Networks
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Germany,France,Italy,United Kingdom,Spain,Slovakia,Portugal
Call: MSCA Doctoral Networks 2021 (HORIZON-MSCA-2021-DN-01, 2021)
Start date: 2023-03-01
End date: 2027-02-28
Duration (months): 48.0
Total project cost (EUR): 2170391.66
EU funding (EUR): 2062404.0
Participants: 15
Project status: SIGNED

TRAIL strategically focuses on a novel, highly interdisciplinary and cross-sectorial research and training programme for a better understanding of transparency in deep learning, artificial intelligence and robotics systems. In order to train a new generation of Doctoral Candidates to become experts in the design and implementation of transparent, interpretable neural systems and robots, we have built a highly interdisciplinary consortium, containing expert partners with long-standing expertise in cutting-edge artificial intelligence and robotics, including deep neural networks, computer science, mathematics, social robotics, human-robot interaction and psychology. In order to build transparent robotic systems, these new ESR researchers need to learn about the theory and practice of the principles of (1) internal decision understanding and (2) external transparent behaviour. Since the ability to interpret complex robotic systems needs highly interdisciplinary knowledge, we will start, on the decision level, to interpret deep neural learning and analyse what knowledge can be efficiently extracted. At the same time, on the behaviour level, the disciplines of human-robot interaction and psychology will be key in order to understand how to present the extracted knowledge as behaviour in an intuitive and natural way to a human user to integrate the robot into a cooperative human-robot interaction. A scaffolded training curriculum will guarantee that the ESRs have not only a deep understanding of both research areas, but experience optimal skill training to be fully prepared for a successful research career in academia and industry. The importance and need of this research for the industry is clearly visible with the full commitment of 7 leading European and world-wide-operating robotics companies that together cover the majority of Europe’s robot market and a broad spectrum of AI applications.

Keywords: neural networks,explainable ai,neural robotics,transparent artificial intelligence systems
Sea water Electrolysis by AEM technology For VariOus Liquid feeds without pre-Treatment (Sea4Volt)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Climate, Energy and Mobility
Theme: Public-private partnerships
Destination: Clean Hydrogen
Action type: Research and Innovation actions
Partnership: CLEANH2 (JU-JTI)
Countries involved: Finland,Germany,Spain,Poland,Estonia,Israel,Ireland
Call: Development of innovative technologies for direct seawater electrolysis (HORIZON-JTI-CLEANH2-2024, 2024)
Start date: 2025-09-01
End date: 2028-08-31
Duration (months): 36.0
Total project cost (EUR): 4370723.43
EU funding (EUR): 3996557.0
Participants: 8
Project status: SIGNED

The main objective of the Sea4Volt project is the development of a novel low temperature Anion Exchange Membrane (AEM) electrolyser concept, able to operate efficiently, selectively, and durably with a direct seawater feed under a slight pH-gradient. Reaching this will require identifying and developing new suitable materials (catalysts, membrane, coatings, porous transport layers, bipolar plates, sealings), as well as novel electrolyser design options. The Sea4Volt will develop and demonstrate a direct seawater electrolyser prototype with novel materials/components and membrane/ionomers to reach effective high-performing and corrosion-resistant seawater electrolysis system. Results of in-operation tests will be published in public deliverables, workshops, and conferences, making it possible for the partners outside of Sea4Volt consortium to exploit leading to a wider impact throughout the European electrolyzer and fuel cell industry. The choice of the newly emerged AEM technology proposed in this project, on one hand, emphasises the extensive innovative technological impact exhibited in the implementation of novel non-CRM materials, PFAS-free anion exchange membranes and ionomers, new electrode designs and protective coatings. On the other hand, the intrinsic cost-effectiveness of the AEM technology, embedded in utilization of low-cost materials, is expected to provide further cost reductions to such an offshore electrolyser system, and will result to anticipated lower cost of green hydrogen production. The technology enabling the generation of green hydrogen directly from seawater holds immense societal-wide impacts. Shift towards green hydrogen production could also stimulate economic growth through the creation of new industries and job opportunities, particularly in regions with abundant seawater resources. Sea4Volt is also being targeted in the areas characterised with deficit of fresh water especially in underdeveloped regions around the globe.

Keywords: electrolysis,seawater,anion exchange membrane electrolyzer,brine
Smart Human-centred Effortless support for Professional clinical Applications (SHERPA)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Health
Theme: Public-private partnerships
Destination: Innovative Health Initiative
Action type: Research and Innovation actions
Partnership: IHI (JU-JTI)
Countries involved: Netherlands,France,Spain,Austria,Belgium,Germany,Switzerland
Call: User-centric technologies and optimized hospital workflows for a sustainable healthcare workforce (HORIZON-JU-IHI-2024-07-single-stage, 2024)
Start date: 2025-01-01
End date: 2028-12-31
Duration (months): 48.0
Total project cost (EUR): 21466301.25
EU funding (EUR): 13542420.0
Participants: 16
Project status: SIGNED

SHERPA will empower interventional radiologists (IR), an overburdened group of specialists who use medical imaging and image-guided devices to perform complex, high-risk interventions. Since trust in technology is rooted in relationships – not in a technical specification or feature, AI-powered assistive technologies will be delivered as a seamless, trusted companion (a ‘sherpa’) across the workflow for two clinical domains (Neurology and Oncology), validated with IR and patients through seven clinical studies, and made available to other medical specialities through a framework methodology and outreach to the entire IR community. The automated workflows and their benefits at user, patient and system levels will be evaluated for two clinical applications: 1. Minimally invasive neurovascular interventions (brain aneurysms) and 2. Minimally invasive interventional oncology (liver tumour ablations). Fueled by cutting-edge advances in AI and robotics yet profusely human-centric, workflow automation will minimise the risk of errors and inconsistencies, ensuring the accuracy and reliability of clinical decisions and boosting IR confidence and job satisfaction. As repetitive, time-consuming tasks can be performed automatically, the IR will gain the much-needed relief to focus on the intervention and the interaction with the patient; the expertise threshold will be decreased, improving workload distribution and team dynamics. Through generalising principles and methodological framework, the insights from SHERPA will extend their benefits to other medical specialties dealing with complex workflows and critical decision-making.

Keywords: ai based assistive technologies image guided therapy interventional radiology workflow automation digital tools
Wireless Test fixtures to Measure the Dielectric Properties of Materials in RF (RFmatCarac)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Countries involved: France
Call: ERC PROOF OF CONCEPT GRANTS (ERC-2023-POC, 2023)
Start date: 2024-07-01
End date: 2025-12-31
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: SIGNED

RF characterisation of dielectric materials is increasingly an important scientific and commercial activity insofar as even if a very significant effort of the RF community has allowed the development of reliable characterisation solutions, it remains that these techniques rely on RF test fixtures that is heavy to implement, expensive, with a limited field of use (type of materials, frequencies, resolution…) and therefore not adapted to the ever-increasing needs of companies, most of which have neither advanced skills in the field nor the equipment to implement these characterisation methods. Indeed, the explosion of the IoT and thus of wireless communication solutions has given rise to the need for companies in a wide range of sectors (not only specialised in electronics and even less in RF) to have a better understanding of the RF performance of the materials they use or wish to use for the fabrication of their devices. For cost or supply reasons, these companies are forced to use materials that are not necessarily suitable for RF (or simply whose RF characteristics are not known), typically plastics that under the same name may in fact have very different dielectric characteristics, particularly in terms of losses, and therefore pose a problem. The idea of the project is to demonstrate the technical and economic viability of a new solution for RF characterisation of dielectrics. This approach breaks with the techniques currently used insofar as it will make it possible to be able to characterise a larger number of materials (plates, solids, liquids, powders, etc.) at a lower cost compared with current solutions. We hope that in a few years this solution will become the reference solution for a large number of companies that until now have not access to these test fixtures.
Digital Process Chain for Energy Renovations Using Prefabrication (DigiFab)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Climate, Energy and Mobility
Theme: Energy
Destination: Efficient, sustainable and inclusive energy use
Action type: Innovation actions
Countries involved: Ireland,Austria,Italy,Spain,Greece,Belgium,Poland
Call: Low-disruptive renovation processes using integration of prefabricated solutions for energy-efficient buildings (HORIZON-CL5-2024-D4-01, 2024)
Start date: 2025-01-01
End date: 2028-12-31
Duration (months): 48.0
Total project cost (EUR): 5776344.0
EU funding (EUR): 4999542.75
Participants: 15
Project status: SIGNED

The DigiFab consortium will develop a renovation process consisting of: 1) Digital process chain with detailed assessment and co-creation methodology to cover the entire renovation workflow; 2) Choice of adaptable prefabricated modules with passive and active technologies to achieve near zero energy building (NZEB) levels and reduced environmental impact; 3) Assessment and monitoring software with building information modelling (BIM) to automate the process. Estimated installation time is 2 days per unit of 200m2 surface, with 30% less cost than conventional renovation. Our solution transforms the renovation process by digitizing it from design brief to finalization. The approach, not widely adopted in the sector, offers significant cost and time reduction by accurately collecting and centralizing data at all stages, minimizing uncertainties that cause delays and increase costs. Co-creation with occupants gathers expectations and best renovation times. Building data is collected with accurate methods, transformed into a high-quality digital twin that automates energy and structural analysis, leading to design optimization with the best technology combinations according to project inputs. Results from the analysis are used directly to manufacture the prefabricated modules using modern methods of construction (MMC) and “just in time” (JIT). The modules use novel lightweight concrete mixes with recycled materials, bio-based materials, active envelope heating technologies, energy generation and management including ventilation. Installation follows specific site conditions, monitored through procedures such as laser scanning. A worksite solution is used to manage and monitor levels of construction demolition waste, and noise. At the end of the commissioning process, energy performance documentation is provided. Three demonstration sites in Spain, Austria and Greece with diverse building typologies will showcase how the DigiFab solution benefits stakeholders.

Keywords: digitalization of construction bim building renovation prefabrication co creation
Data Enablement and Navigation Information Service (DENIS)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Accelerator
Destination: Other destinations
Action type: EIC actions
Countries involved: United Kingdom
Call: EIC Accelerator 2024 - Short application (HORIZON-EIC-2024-ACCELERATOR-01, 2024)
Duration (months): 12.0
Total project cost (EUR): 1100000.0
EU funding (EUR): 1100000.0
Participants: 1
Project status: SIGNED

In a world inundated with data, the key to societal advancement lies not just in collecting data, but in making it truly accessible and actionable. Founded in the UK by Adam and David Milward, MetadataWorks (MDW) embarked on a mission to solve this challenge for the public sector, making their data Findable, Accessible, Interoperable, and Reusable. Drawing from rich backgrounds in IBM and NATO, as well as a PHD from Oxford University, the co-founders developed an open-source toolkit that was utilised on groundbreaking projects like the Oxford Biomedical Research Centre and the 100,000 Genomes Project. But the journey didn't stop there. With seed investment, MDW has developed into a human-centric data catalogue that is being utilised to manage data for the IPCC, Office for National Statistics, National Health Service, Genomics England and many more leading public sector organisations. DENIS–the Data Enablement Navigator & Information Service is the next stage in MDWs journey. DENIS isn't just software; it’s a new paradigm in data management, that builds on MDWs metamodel which was recognized for its innovation at the International Conference on Model Driven Engineering in 2017. DENIS redefines data governance, offering an automated solution for the creation, maintenance, and distribution of essential data knowledge, including data dictionaries, glossaries, and reference materials. It proficiently categorizes data by aligning it with existing knowledge assets and astutely recommends new definitions in areas where gaps are identified. This automation significantly cuts down on time and resources required to make data FAIR. Functioning on a secure, federated network, DENIS effortlessly interconnects and administers data knowledge across various domains, thereby bolstering operational efficiency and reducing expenditures in larger distributed organisations. DENIS isn't just a system; it's a data revolution, fostering a smarter, fairer, and more efficient future.

Keywords: metadata,data governance,data lineage,classification,ai,data enablement