IBM IRELAND LIMITED

Glass fibre production, precision machining of large parts (e.g. wind turbines), additive manufacturing of medical implants and high-temperature metal production are manufacturing examples with processes that are difficult to automate. The main reasons for the actual labour intensive efforts in these scenarios are lack of full understanding and control over the individual manufacturing steps and the high complexity of the tasks. This has severe impacts on sustainable growth, manufacturing productivity, efficiency and flexibility due to the large amount of unpredictive waste in production and processing time. COGNIMAN is devoted to improving these situations by developing and demonstrating a novel concept of ?digital cognitive smart manufacturing? that will shift the future design of manufacturing processes towards autonomous and predictive manufacturing with improved flexibility, safety and efficiency. This initiative will provide the means to facilitate flexible, resilient, reconfigurable, safe, sustainable, and efficient smart manufacturing by integrating key technologies. They include simulations, digital twins, advanced sensors, machine learning toolbox and cognitive robotics integrated in human-centric modular toolboxes that can be easily adapted to substitute varying manual manufacturing processes. By tackling significant challCOGNIMAN will provide the means to facilitate flexible, resilient, reconfigurable, safe, sustainable, and efficient smart manufacturing by integrating simulation, models, digital twins, sensors, Artificial Intelligence (Machine Learning), data processing and analytics, robotics, and autonomous systems in a human-centric modular toolbox that can be easily adapted to new manufacturing processes and environments with the ultimate objective of boosting the European technology and manufacturing sectors competitiveness towards industrial leadership in global markets, as well as to reduce the environmental footprint of manufacturing activities.

EC contribution:

EUR 511k

Start:

2023-01-01

End:

2026-12-31

Status:

SIGNED

Scheme:

HORIZON-IA

Call:

HORIZON-CL4-2021-TWIN-TRANSITION-01

View on CORDIS (DOI 10.3030/101058477)

The rapid expansion of EU regulations such as GDPR, AI Act and Data Act creates major challenges for organisations that must continuously demonstrate compliance. Manual interpretation of legal texts, fragmented reporting tools, and siloed infrastructures increase administrative costs and risks, particularly for SMEs. In parallel, the emergence of European data spaces and AI-driven services requires secure, transparent and interoperable infrastructures where compliance is embedded as a core property. RegulAIze delivers an end-to-end framework for regulatory compliance, creating a smart and trustworthy data ecosystem in which organisations can exploit AI-powered technologies across the compliance lifecycle. This is achieved through automated compliance mechanisms, NLP-based legal assistants, privacy-enhancing technologies, secure and auditable data transactions, semantic interoperability layers, and vector-database reasoning engines. The project focuses on seven objectives: (1) transform legal texts into machine-readable obligations using explainable LLMs; (2) issue verifiable Compliance Cards as blockchain-anchored credentials for datasets, AI models and processes; (3) integrate federated learning, fully homomorphic encryption and zero-knowledge proofs for secure data operations; (4) build synthetic data pipelines and digital twin validation to ensure representativeness and bias mitigation; (5) deploy compliance knowledge graphs with advanced vector search for semantic reasoning; (6) establish the RegulAIze Academy for training and skills development; and (7) validate the framework in six pilots across mobility, tourism, AI factories, legal services and cross-sector domains. By being fully aligned with Horizon Europe Destination 3, RegulAIze incorporates automated verification into European data spaces to reduce compliance costs, build trust in AI and data services and enable scalable, privacy-preserving innovation.

EC contribution:

EUR 470k

Start:

2026-05-01

End:

2029-04-30

Status:

SIGNED

Scheme:

HORIZON-IA

Call:

HORIZON-CL4-2025-03

View on CORDIS (DOI 10.3030/101298664)

One of the key enablers of 6G is undoubtedly the Native support of AI/ML at all the system levels, components, and mechanisms, from the orchestration and management levels to the low-level optimization of the infrastructure resources, including Cloud, Edge, RAN, Core Network, as well as a transport network. Despite the opportunities, there are several gaps that hinder the adoption of AI/ML in 6G, such as the lack of extensive and high-quality datasets that are required to train the models. On the other hand, AI model testing and performance evaluation in a representative staging environment (by emulation or real deployment) is also challenging without access to an end-to-end 6G testbed or representative Digital Twin environment. To this end, 6G-DALI aims to deliver an end-to-end AI framework for 6G, structured in two interdependent pillars, (1) AI experimentation as a service via MLOps and (2) Data and analytics collection and storage via DataOps. The 6G-DALI DataOps pillar provides the mechanisms for preparing clean and processed data that are stored within a 6G Dataspace and are made available for training and validating machine learning models as a service, a part of the MLOps Pillar. The end-to-end framework also delivers continuous monitoring, drift detection and retraining of models. Finally, 6G-DALI will deliver open datasets, a 6G Dataspace for dataset storage and secure sharing, and a Digital Twin testbed for data generation on demand.

EC contribution:

EUR 408k

Start:

2025-01-01

End:

2027-12-31

Status:

SIGNED

Scheme:

HORIZON-JU-RIA

Call:

HORIZON-JU-SNS-2024

View on CORDIS (DOI 10.3030/101192750)

Artificial Intelligence (AI) is widely studied and finding increasing adoption across communication technologies spanning network layers and business ecosystems. It is anticipated to play a central role in the design and operation of future 6G networks. Despite the promise of AI, there remain many obstacles to its use in communication networks. The introduction of software defined elements such as radio access network (RAN) intelligent controllers (RIC) enables multi-party applications for the control and management of networks. However, AI functions are still nascent and such structures do not extend to optical networks or multi-controller environments. 6G-XCEL seeks to address these challenges through research on high edge network use cases that employ multi-party AI controls running over compute accelerators to coordinate control across radio and optical networks. It will develop a reference framework for AI in 6G that will pave the way towards global validation, adoption and standardisation of AI approaches. This framework will enable decentralised AI-based network controls across network domains and physical layers, while promoting security and sustainable implementations. Using the latest AI algorithms and data compression, research on the resulting decentralised multi-party, multi-network AI (DMMAI) framework will enable the development of reference use cases, data and model repositories, curated training and evaluation data, as well as technologies for its use as a benchmarking platform for future AI/ML solutions for 6G networks. 6G-XCEL will bring together a large ecosystem of researchers from the EU and US to implement elements of the DMMAI framework in their testbeds and labs, integrating it into their research programs and validating the framework across platforms. Working with standardisation groups within each jurisdiction, 6G-XCEL will achieve joint progress towards large scale application of AI in 6G networks.

EC contribution:

EUR 335k

Start:

2024-01-01

End:

2026-12-31

Status:

SIGNED

Scheme:

HORIZON-JU-RIA

Call:

HORIZON-JU-SNS-2023

View on CORDIS (DOI 10.3030/101139194)

We live in a crucial historical moment, with tremendous challenges ahead, from climate change to the energy crisis. ELIAS emerges from the belief that AI will be a key discipline to help us tackle these challenges. At the same time, the development of AI entails deep ethical and societal concerns that need to be addressed. As for fundamental research, ELIAS will address key scientific questions about how AI can reduce computational costs, serves to model effects of policy decisions on society, and impacts individuals. ELIAS will strive for a deep integration of the fundamental research that takes place in academia and the more applications-focused research from industry. ELIAS builds on and expands the highly successful and internationally recognized European Laboratory for Learning and Intelligent Systems (ELLIS). ELIAS will further develop the excellence criteria and the pillars in ELLIS and implement actions that will support AI researchers and young talents at different stages of their careers. Furthermore, ELIAS will develop a Sciencentrepreneurship track, with the purpose of attracting and empowering talents at the interface of scientific innovation and business and establish original AI solutions that move towards a sustainable long-term future for our planet, contribute to a cohesive society, and respect individual rights. The outcome of ELIAS will be to establish Europe as a leader in AI research in which impact on the environment, society and the individual are integral considerations during development. We will measure the success of this endeavor in terms of key indicators, including the number of new cross-institutional collaborations, the number of cross-disciplinary collaborations, the number of industry-academic partnerships, publications in top conferences and journals, patents, and the number of projects that have resulted in deployed technologies.

EC contribution:

EUR 205k

Start:

2023-09-01

End:

2027-08-31

Status:

SIGNED

Scheme:

HORIZON-RIA

Call:

HORIZON-CL4-2022-HUMAN-02

View on CORDIS (DOI 10.3030/101120237)

Machine learning methods operate on formal representations of the data at hand and the models or patterns induced from the data. They also assume a suitable formalization of the learning task itself (e.g., as a classification problem), including a specification of the objective in terms of a suitable performance metric, and sometimes other criteria the induced model is supposed to meet. Different representations or problem formalizations may be more or less appropriate to address a particular task and to deal with the type of training information available. The goal of LEMUR is to create a novel branch of machine learning we call Learning with Multiple Representations. We aim to develop the theoretical foundations and a first set of algorithms for this new paradigma. Moreover, corresponding applications are to demonstrate the usefulness of the new family of approaches. We regard LEMUR as very timely, as LMR algorithms will allow to flexible representations (e.g., suitable for explainability, fairness) with diverse target functions (e.g., incorporating environmental or even social impact) so as to make the induced models abide by the Green Charter and trustworthy AI criteria by design. We will focus on learning with weak supervision because it addresses one of the major flaws of modern ML approaches, i.e., their data hunger, by means of weaker sources of labelling for training data. The outcome of the DN will be a set of 10 experts trained to implement the third and subsequent waves of AI in Europe. The highly interdisciplinary and intersectoral context in which they will be trained will provide them with research-related and transferable competences relevant to successful careers in central AI areas.

EC contribution:

EUR 0

Start:

2023-01-01

End:

2026-12-31

Status:

SIGNED

Scheme:

HORIZON-TMA-MSCA-DN

Call:

HORIZON-MSCA-2021-DN-01

View on CORDIS (DOI 10.3030/101073307)

CloudStars is an Open Source Research Mobility network in the field of Cloud Computing technology. The proposal combines eleven academic institutions in nine European countries (Spain, Portugal, UK, Germany, Netherlands, Italy, Austria, Poland, Switzerland), two companies in Europe (SAP in Germany, NearBy Computing in Spain), and three industrial laboratories from IBM (USA, Switzerland, and Israel). The major flow of secondments is scheduled between the academic institutions and IBM industrial labs, but also including a significant number of secondments to SAP and NearBy Computing. CloudStars will create a global reference community in open source Cloud Computing. The participants will combine theoretical skills and experience in distributed systems research with industrial open source technologies and cutting-edge Cloud and Edge infrastructures. This will increase the overall global impact of research contributions, helping to arrive to millions of interested third parties through open source communities. The general goals of the project are: 1. Increase the impact of European researchers with contributions to key open source projects and the involvement in open source communities. 2. Rise the careers of European researchers through well-established networks both across EU and with global open source players. 3. Increase the reproducibility of results in Science and Data Analytics through standard Cloud container technologies. Technical goals are: 1. Development and benchmarking of next generation container technologies leveraging open source Cloud Native Computing Foundation (CNCF) projects and GAIAX protocols. 2. Design novel cloud serverless middleware over container technologies including Function as a Service, serverless containers, and event-based orchestration. 3. Apply novel machine learning techniques for managing containerized Cloud and Edge systems, involving the infrastructure and configuration of executions and services.

EC contribution:

EUR 0

Start:

2023-01-01

End:

2026-12-31

Status:

SIGNED

Scheme:

HORIZON-TMA-MSCA-SE

Call:

HORIZON-MSCA-2021-SE-01

View on CORDIS (DOI 10.3030/101086248)