AI-PRISM is an industrial-end-user driven project that will provide a human-centred AI-based solutions ecosystem targeted to manufacturing scenarios with tasks difficult to automate and where speed and versatility are essential. The result will be an integrated and scalable ecosystem with installation-specific solutions for semi-automated and collaborative manufacturing in flexible production processes and for which specific robotic programming skills will not be required, thanks to its programming-by-demonstration modules. The ecosystem will be composed by four main pillars including 1) Human Centred Collaborative Robotic Platform, 2) Human Robot Cooperation Ambient, 3) Social Human-Agent-Robots Teams Collaboration and 4) Open Access Network Portal. In order to facilitate the assessment of the performance, transferability, scalability and large-scale deployment of these solutions, the demonstrations will be conducted under real operational environments in four pilot involving key manufacturing sectors - Furniture (ES), Food/Beverage (GR), Built-in Appliances (TR) and Electronics (PL) -, plus one generic demonstration facility (AT). The project is not just aiming at quantitative improvements in a specific sector, but to use technology innovation to support a change of paradigm where AI, robotics and Social Sciences and Humanities (SSH) integrated in the manufacturing domain for the improvement of flexible production processes, become a feasible and widespread alternative for European factories, especially SMEs. To achieve this, the project relies on a strong consortium of 25 partners from 12 countries including international cooperation with Korea. The consortium brings together all the actors of the Human Robot Collaboration (HRC) value chain including relevant competence centres, technology providers, equipment providers, integrators, and manufacturers/end users; and involves key expert partners in SSH, standardisation, exploitation, and dissemination.

The sustainability of the European health care sector is challenged by 6 mega trends and as a consequence, the health care sector has for several years been undergoing major changes becoming increasingly digitalized, streamlined and focused on more staff and patient involvement and patient responsibility, in order to gain maximum quality in care, patient safety, efficiency, and at the same time stay economically sustainable.EUVECA is designed to support the needed innovation and development of the health care sector, by ensuring the provision of future oriented skills within the sector. This will be done through the creation of Regional Vocational Excellence Hubs in 7 European regions, which collaborate within a European Platform for Vocational Excellence in Health Care. The objective is to ensure coordination, adaption, innovation and upward convergence within regional health education eco systems, as well as promoting European (blended) mobility and inter regional learning and collaboration among health care professionals and students from the participating regions. EUVECA will show that by including a strategic and coordinated focus on VET in the work and collaboration within the regional health eco-systems, VET can contribute in a substantial way to move innovation forward within the European health care sector and ensure the sustainability of the sector on both a European and regional level. EUVECA will be implemented by a multi-disciplinary consortium of professionals, academics, local and regional health authorities, regional development agencies and social partners. Within 8 work packages, the partners and their regional stakeholders will co-create a governance model, as well as concrete education and training activities for the 7 regional hubs and the European platform will be developed, tested, fine-tuned and made sustainable for uptake of regional partnerships beyond the partnership.

The strong interaction is one of the cornerstones of the Standard Model (SM) of particle physics, and its experimental and theoretical study attracts an active community of about 2500 researchers in Europe. The list of fundamental open questions at the frontier of our current knowledge in the strong interaction is very rich and varied including a full understanding of (i) the partonic structure of hadrons, (ii) exotic hadronic states, properties of (iii) dense quark matter and of (iv) hot and dense quark-gluon plasma, as well as (v) precision tests of the SM. Such research topics are studied experimentally and theoretically mostly via particle collisions at low (a few tens of GeV) and high (up to 14 TeV) energies. Associated developments in state-of-the-art detectors/data-acquisition/beams/targets are required, as well as in theoretical (lattice, effective field, perturbative) calculations. The STRONG-2020 project brings together many of the leading research groups and infrastructures involved today in the study of the strong interaction in Europe, and also exploits the innovation potential in applied research through the development of detector systems with applications beyond fundamental physics, e.g. for medical imaging and information technology. The Consortium includes 44 participant groups, embracing 14 EU Member States, one International EU Interest Organization (CERN), and one EU candidate country. Together with host institutions of 21 other countries, without EU funds benefits, the project involves research in 36 countries. The project is structured in 32 Work Packages (WP): 7 Transnational Access Activities, 2 Virtual Access Activities, 7 Networking Activities and 14 Joint Research Activities. Furthermore, 2 WPs take care, respectively, of the “Management and Coordination” of the project and of “Communication and Outreach".

ELOQUENCE is focused on the research and development of innovative technologies for collaborative voice/chat bots. Voice assistant-powered dialogue engines have previously been deployed in a number of commercial and governmental technological pipelines, with a diverse level of complexity. In our concept, such a complexity can be understood as a problem of analysing unstructured dialogues. ELOQUENCE’s key objective is to better comprehend those unstructured dialogues and translate them into explainable, safe, knowledge-grounded, trustworthy and bias-controlled language models. We envision to develop a technology capable of learning by its own, by adapting from a very data-limited corpora to efficiently support most of the EU languages; from a sustainable computational framework to efficient and green-power architectures and, in essence, that may serve as a guidance for all European citizens whilst being respectful and showing the best of our European values, specifically supporting safety-critical applications by involving humans-in-the-loop. Overall, ELOQUENCE’s project considers building on top and to improve of prior achievements in the domain of conversational agents, e.g. recently launched and public-domain Large Language Models (LLMs), such as chatGPT (e.g., more recent versions), or LaMDa most of them developed in non-EU countries. While including key industrial enterprises from Europe (i.e., Omilia, Telefonica, Synelixis), ELOQUENCE will validate the developed technology through (i) safety-critical scenarios with human-in-the-loop for security-critical applications (i.e., emergency services in call centres) and (ii) smart home assistants via information retrieval and fact-checking against an online knowledge base for lesser risky autonomous systems (i.e., home-assistants). ELOQUENCE will target the R&D of these novel conversational AI technologies in multilingual and multimodal environments and demonstrated in several pilots.

Green hydrogen is one of the most promising solutions for the decarbonisation of society. Alkaline water electrolysis (AWE) is already a mature technology but its large footprint makes it inadequate for producing the energy vector at GW scale. Proton exchange membrane water electrolysis (PEMWE) on the other hand is compact but its dependence on iridium and other expensive materials poses a serious threat for up-scaling. Anion exchange membrane water electrolysis (AEMWE) combines the benefits of both technologies. However, its key performance indicators (KPI) do not reach commercial requirements and are lacking competitiveness. NEWELY project aims to redefine AEMWE, surpassing the current state of AWE and bringing it one step closer to PEMWE in terms of efficiency but at lower cost. The three main technical challenges of AEMWE: membrane, electrodes and stack are addressed by 3 small-medium-enterprises (SME) with their successful markets related to each of these topics. They are supported by a group of 7 renown