Ερευνητικά Έργα

AIRIS will develop the next generation of multimodal Generative AI (GenAI) models to accelerate research on predictive and personalised medicine. Building on recent advances in LLMs, causal inference and mechanistic modelling, AIRIS will integrate heterogeneous biomedical data (omics, imaging, clinical, laboratory, lifestyle, PROMs and scientific literature) into mechanism-informed generative frameworks. These models will not only generate biologically plausible synthetic data to address sparsity and bias but also embed causal and dynamical constraints across biological scales, from “virtual cells” to multi-organ models, enabling counterfactual reasoning and in-silico hypothesis testing. The project will deliver (i) robust agentic multimodal data integration pipelines; (ii) novel mechanism-anchored generative architectures with interpretability and bias-mitigation safeguards; (iii) hypothesis generation assistants for biomarker discovery, drug repurposing, and intervention design; and (iv) a scalable MLOps infrastructure ensuring reproducibility. AIRIS will be validated using already existing large-scale datasets in five high-impact use cases including Pulmonary Fibrosis, Steatotic Liver Disease, Cardiovascular Disease, Chronic Kidney Disease and Inflammatory Bowel Disease. AIRIS will demonstrate utility in modelling of disease, while also allowing cross-disease insights. A multidisciplinary consortium spanning leading universities and research centres, HPC experts, industry, and SME ensures excellence across the project dimensions. ELSI and fairness will be systematically addressed by SSH and legal experts. Outputs will be openly disseminated through benchmarks, training, and open-source releases, fostering European leadership in mechanism-informed GenAI. AIRIS will maximise scientific, clinical, and societal impact by bridging AI innovation with biomedical discovery, improving disease understanding, and supporting equitable personalised care.

The K2W project aims to enhance career readiness and foster stronger industry-academic collaboration in the Western Balkans. By aligning academic curricula with the evolving demands of the labor market, the project seeks to address existing skills gaps, ensuring that university graduates are better prepared for the workforce. Key components of the project include the establishment of career centers, the development of the SkillsBox platform, and the implementation of curriculum reforms that integrate career readiness and life skills into academic programs. Through the establishment of modern career services and training tools, the K2W project aims to not only address current labor market demands but also contribute to regional education reforms, making the Western Balkans more competitive on the international stage. The project’s impact will be evaluated regularly, ensuring that its outcomes continue to meet the needs of students and the labor market in the long term

ARCHIPELAGO aims to enhance the digital capacity of higher education institutions (HEIs) in Small Island Developing States (SIDS) by developing and implementing targeted digital education solutions. The core objective is to overcome the geographical, infrastructural, and resource-based challenges faced by these regions, improving the accessibility, quality, and sustainability of higher education. By addressing these specific challenges, the project strives to promote greater educational equity and foster regional cooperation through innovative digital tools. The project focuses on three primary objectives: first, to develop digital toolkits for faculty, administrative staff, and students, aimed at enhancing digital literacy, teaching methodologies, and e-administration practices. Second, to establish an asynchronous MOOC platform, providing students with essential digital skills and preparing them for online learning environments. Lastly, the project aims to create a collaborative forum that facilitates communication and cooperation across SIDS universities, encouraging the exchange of research, best practices, and policy development. Participating countries in the project include Fiji, Trinidad and Tobago, Comoros, Greece and Slovakia. These countries are central to the project’s objective of strengthening the digital infrastructure in SIDS, ensuring that universities are better equipped to provide quality education in a rapidly changing global landscape. Through these initiatives, the project aims to build long-term educational resilience in SIDS, contribute to regional development, and enhance opportunities for collaboration and knowledge sharing across the region.

CyberAId aims to significantly enhance cybersecurity resilience in Europe's critical financial infrastructure by deploying advanced Artificial Intelligence and state-of-the-art cybersecurity tools and services. By leveraging breakthrough technologies such as Generative AI, Large Language Models (LLMs), Blockchain analytics, and Quantum security, CyberAId addresses common and emerging cybersecurity challenges within the financial industry through integrated, scalable solutions.

The primary goal of FOSSBot4AI is to offer higher education students and instructors a practical educational method for Artificial Intelligence and educational robotics. This approach is grounded in the Five Big Ideas in AI framework (https://ai4k12.org/) and involves a range of AI/ML/IoT projects, a comprehensive and accessible educational package, and a virtual simulator. The project aims to enhance students' computational, analytical, and algorithmic thinking abilities. The project seeks to produce educational resources, a 3D-printed easy to assemble educational robot, and a virtual simulation environment. It is structured into 5 distinct work packages, each with specific goals and tasks. Implementation will occur incrementally, with participant feedback integrated throughout each phase. The collaboration will involve joint efforts to develop, evaluate, and disseminate the project's outcomes across three phases, including meetings, deliverables, and testing.

The project’s general objective is to support a prosperous twin green & digital transition in the Multi regions with specific reference to Kenya, Western Balkan Countries contexts by comprehensive and effective development and uptake of green and digital skills needed for a modern workforce that can contribute to economic growth. This will be done by designing, developing and piloting an innovative competency-oriented learning ecosystem that will help future generations acquire the skills they need to meet the challenges and opportunities of the twin green & digital transition and elevate their positive impact on society.

SALUS addresses the growing security challenges of IoT systems by providing law enforcement with (i) new forensic investigation schemes and policies, and (ii) advanced IoT forensic tools for threat detection, evidence collection, and cross-agency collaboration, all while ensuring compliance with EU legal and ethical frameworks. To implement this, the project combines a novel Digital-Twin (DT) infrastructure for proactive threat simulation, a secure Software Defined Network-enabled IoT architecture (SDaaSS) acting as the backbone of the DT for dynamic policy enforcement, and AI-powered forensic capabilities for real-time IoT device detection, lawful evidence interception, and blockchain-based chain of custody. Validated through five diverse pilot use cases in collaboration with five (5) police authorities and two (2) critical infrastructure providers (hospitals and nuclear power plants), SALUS bridges technology and operational needs to enhance security in critical infrastructures and IoT ecosystems. The above technological developments will be closely accompanied by activities concerning: i) incorporation of legal and ethical aspects, including fundamental rights, privacy, personal data, etc, ii) integration of operational aspects, alignment with relevant EU cybersecurity policies, analysis of modus operandi, lawful evidence exploitation, and delivery of policy recommendations for tackling new and emerging forms of IoT-related crime, all aimed at improving police authorities’ understanding, iii) development of multi-dimensional, comprehensive practitioner training activities and joint exercises, tailored to the organization of operational-level hackathon activities, iv) Development of investigation, technological and security standards, and v) Establishment of a synergistic ecosystem among related national and EU-funded projects, as well as stakeholders from the law enforcement community.

The goal of this study will be to examine the behavioral compensatory changes that allow human participants to minimize body weight gain in conditions of sustained daily supplementation with almonds. The absence of weight gain has been well documented with almond supplementation reaching 50-100 g over several months. To achieve our goal, 100 women will be recruited and randomized to either an almond supplementation group (n=50) or a control group whose daily diet will be supplemented with an equicaloric load of a savory biscuit (n=50).

MOSAIC addresses a grand challenge for European competitiveness: technological independence and filled fabs in the landscape of automated systems. By fostering innovation in Electronic Components and Systems (ECS), MOSAIC aims to propel Europe to excellence and digital autonomy, directly linked to the EU Chips Act. The project achieves this through a comprehensive strategy. It will develop next-generation ECS offering superior, cognitive system intelligence, enabling energy efficiency and robustness. These results will be tailored to the demands of automated systems, enabling rapid data processing and intuitive, AI-enabled decision- making. MOSAIC tackles the challenge of integrating diverse perception hardware configurations, ensuring that automated systems can perceive their surroundings in a non-invasive manner, avoiding a single point of failure, with unparalleled accuracy and decreased complexity. Additionally, the project emphasizes standardized communication protocols and interoperability, fostering a collaborative ecosystem across several industries, namely automotive, aerospace, maritime, industrial automation and infrastructures. By spearheading such advancements, MOSAIC empowers European ECS manufacturers to gain a competitive advantage. The project's achievements will be demonstrated in 31 cutting-edge technical showcases, indicatively global perception through 360° distributed radar, AI-enabled reasoning through magnetic field signature and resilient communications by means of non-terrestrial networks. 2 accompanying impact studies, will solidify Europe's position as a global leader in automated systems. MOSAIC leverages a pan-European consortium encompassing the entire ECS value chain, ensuring a comprehensive effort towards filling the European fabs and ensuring digital sovereignty. In essence, MOSAIC is an investment in Europe's future – a secure digital future of technological leadership, economic prosperity, and strategic independence

The Shift2SDV project aims to revolutionize the European automotive domain by creation of an SDV ecosystem around middleware & API framework enabling collaboration across the automotive value chain. This ambitious endeavour envisions a comprehensive shift towards a modular framework that transcends the limitations of current monolithic systems, fostering agility and innovation through the development of complementary middleware services and software development solutions. Central to Shift2SDV is the development of a cutting-edge middleware framework that provides micro-services to build automotive applications upon, abstracting from underlying hardware components – supporting stepwise migration, open source and proprietary components, in-vehicle safety critical and off-vehicle cloud functionality. It is specifically designed to streamline software development and integration while ensuring compatibility and flexibility with existing and emerging technologies. Key technical objectives include the development of a modern, flexible micro-services-based architecture, middleware framework that simplifies the brand-specific application development, and establishment of a safe and secure system architecture compliant with functional safety standards. Additionally, the project aims to develop an orchestration for efficient resource management, integrate edge and cloud computing, and demonstrate the practical viability of the developed middleware through concrete use cases. To maximize impact, Shift2SDV prioritizes active communication, dissemination, and exploitation of project outcomes, fostering collaboration among stakeholders and existing projects and initiatives aligning technological advancements with market demands. Through these concerted efforts, Shift2SDV seeks to propel European leadership in Software Defined Vehicles, driving innovation and economic growth in the automotive industry.

Εlectrification and autonomy drive the rapid evolution of modern vehicles, requiring increasing computational capabilities, coupled with safety and efficiency. The classical, decentralized multi- Electronic Control Units (ECU) architecture has significant drawbacks when it comes to scalability, and it is becoming untenable. The dominant megatrend pushes for an increasing number of key functionalities to be software-defined, with the direct implication that the software content (lines-of-code) in a vehicle will grow by 10x in just 5 years, to 1 billion by 2030. From a hardware viewpoint, increased complexity and autonomy requires a more centralized approach to on-board computing to curtail cost, latency and bandwidth bottlenecks of the in-vehicle network. Centralizing the E/E architecture requires merging multiple Electronic Control Units (ECUs) into powerful, fully programmable Domain Control Units (DCUs) or Zonal Control Units (ZCUs). To address this paradigm shift, the Rigoletto project will establish the foundation for a next-generation Automotive Hardware Platform based on the open RISC-V instruction set architecture (ISA), bolstering and securing Europe's leading role in the automotive electronics industry. The project aligns with the high-level goal of EU Chips Joint Undertaking and the of the industry-led Vehicle of the Future initiative: namely, the creation of a RISC-V based automotive hardware platform strongly linked with the formation of an open, software-defined vehicle ecosystem led by European automotive manufacturers and suppliers. Rigoletto aims at developing RISC-V intellectual property (IP) components, including processor cores, accelerators, interconnects, memory hierarchy and peripheral subsystems. A wide range of performance profiles will be targeted for next-generation DCUs and ZCUs, to enable increasingly electrified, automated, and connected vehicles.

Το έργο συνίσταται ειδικότερα στα εξής: Να σχεδιάσει, να αναπτύξει και να εφαρμόσει εργαλεία και τεχνολογίες για την προστασία συστημάτων UAV (drones) από επιθέσεις κυβερνοασφάλειας, καθιστώντας τα ικανά να αλληλεπιδρούν συνεργατικά με το περιβάλλον τους και να εκτελούν σύνθετες εργασίες σε διάφορους τομείς εφαρμογών της βιομηχανίας και του τελικού χρήστη.

.Η προτεινόμενη παρέμβαση στοχεύει στην ανάπτυξη ενός ολοκληρωμένου, μη παρεμβατικού συστήματος για την ανίχνευση, την παρακολούθηση και τη διαχείριση των επιπέδων στρες των ατόμων που εργάζονται σε περιβάλλοντα που εμπεριέχουν υψηλό ψυχικό φόρτο σε συνδυασμό με πνευματική και σωματική καταπόνηση. Η έρευνα εστιάζει στην ανάπτυξη αλγορίθμων και μοντέλων μηχανικής μάθησης για την ανίχνευση του εργασιακού στρες μέσω δεδομένων που συλλέγονται από διάφορες πηγές, καθώς και στη δημιουργία εκπαιδευτικών προγραμμάτων ευαισθητοποίησης και διαχείρισης του στρες που θα παρέχονται μέσω ψηφιακών πλατφορμών e-learning. Μέσω μιας ειδικά διαμορφωμένης εφαρμογής κινητού τηλεφώνου, οι χρήστες θα ενημερώνονται σχετικά με την αλληλεπίδρασή τους με τα μέρη του συστήματος, ενώ παράλληλα θα έρχονται σε επαφή με στοιχεία παιχνιδοποίησης και προσωποποιημένης παρέμβασης.

By 2030, TRIANGLE will establish a resilient and inclusive pan-European innovation ecosystem that seamlessly integrates academia, industry, and society. Through interdisciplinary collaboration, digital and green skill development, and entrepreneurial capacity building, the ecosystem will empower diverse talent to create impactful, market-ready solutions. It will enhance knowledge transfer, foster startup growth, and drive sustainable economic and societal transformation, positioning the participant countries as leaders in innovation and equitable progress.

The main aim of the project is to reduce e-waste on a Qatari scale. To do so, the project will: ● Generate, for the first time, a clear framework aiding producers to evaluate their choices and pathways to eco-design, to foster Qatari leadership in the green transition. This will lead to an online framework that makes users record the amount of e-waste of different types they produce and recycle, and list the ways they achieve it, allowing us to make recommendations. ● Make green ECS: Provide innovative techniques for reducing, repairing, reusing, refurbishing, recycling to decrease e-waste and boost circularity in a new generation of electronics. This will lead to the establishment of a mobile app building on consortium assets (e.g. the ERF app where users can track their recycling activities), with additional AI tools and methods. ● Showcase green solutions: Demonstrate innovation potential, usability, and versatility of the green solution along the value chain. This will lead to the establishment of 5 kiosks within the QU area, as well as in other pre-agreed areas in Doha, motivating users to actively recycle and buy refurbished products. Also, a digital marketplace will be delivered, where users can interact with each other on buying and selling refurbished electronic devices, with motivations. ● Building consciousness: Create an ecosystem comprising a chain of stakeholders (circularity network), to empower the next ECS generation

Καταγραφή τρέχουσας κατάστασης, διεξαγωγή συνεντεύξεων και ανάλυση σχετικά με τον μακροπρόθεσμο σχεδιασμό στην υγεία, με έμφαση στη χρήση της τεχνολογίας και της τεχνητής νοημοσύνης.

TORNADO will be a ground-breaking cloud robotics system, facilitated by low-latency wireless communications. It is envisioned as installed within a dynamic, time-varying environment where humans are present and pursue their own goals. The TORNADO robot will be deployed at the command of a local human supervisor, in order to execute a sequence of non-repetitive tasks that are being autonomously and adaptively identified based on very high-level human instructions (e.g., verbal). Successful execution of this task plan requires the multifunctional TORNADO robot to be able to generalize well and to dynamically adapt to the current situation and to novel desired functionalities, with regard to its navigation, planning, HRI and manipulation capabilities.

.Along the whole value chain in using data for economic purposes, guidelines and tools are required to make the business of the different stakeholders successful, and the end-users confident that none of their rights are endangered. CERTAIN addresses these needs and delivers solutions for data holders, dataspaces and AI systems providers, and AI systems deployers, which are the primary actors of the data and AI value chain. They must be compliant with applicable European regulations, must reach this compliance in a timely manner, and at reasonable cost. CERTAIN delivers guidelines and technical tools to help with compliance, to assess data quality, to measure biases in datasets, and to protect privacy. CERTAIN sets the foundation of AI certification: it translates the regulations to business terms, builds a directory of certification entities per business, develops a platform to streamline the certification process, and tools for AI system providers and certification entities so that they could respectively prepare and run a certification process. In case of security breach, not only privacy may get compromised, but also AI models may become useless and lead to extremely damageable decisions. To make sure that AI-based products are of high quality and reliability, CERTAIN develops security tools and methods, specifically suitable for dataspaces and AI systems. CERTAIN addresses the environmental footprint of the AI value chain. Innovative techniques are elaborated to reduce energy consumption when building and running AI systems. This is beneficial not only for the green deal but to reduce cost for AI stakeholders. As importantly, CERTAIN considers the end-users perspective, and provides templates and guidelines that may be used by AI systems deployers to reassure end-users on the use of their private data. The project tests its results on seven operational pilots in six different business areas, considering all the actors along the AI value chain.

O πρωταρχικός στόχος του έργου DELTA είναι να καλλιεργήσει μια κουλτούρα ανταλλαγής γνώσεων και να προωθήσει την καινοτομία στη διεπιστημονική διδασκαλία και μάθηση, με εφαρμογή στα Δυτικά Βαλκάνια. Το έργο εστιάζει στον εξοπλισμό των εκπαιδευόμενων με γνώσεις και δεξιότητες σχετικά με θέματα διαχείρισης δεδομένων,Συγκεκριμένα το έργο DELTA προσπαθεί να: • Παρέχει σε φοιτητές από διάφορες σχολές πανεπιστημίων των Δυτικών Βαλκανίων την ευκαιρία να αποκτήσουν ένα ικανό επίπεδο εγγραματισμού σε θέματα δεδομένων. Διασφαλίζει ότι οι μαθητές θα είναι καλά προετοιμασμένοι για να εφαρμόσουν αυτές τις κρίσιμες δεξιότητες σε ένα ευρύ φάσμα σεναρίων τόσο στην προσωπική όσο και την επαγγελματική τους ζωή, ενισχύοντας έτσι την προσαρμοστικότητα και την ανταγωνιστικότητά τους. • Γεφυρώσει την υφιστάμενη διαφορά μεταξύ Δυτικών Βαλκανίων και Ευρωπαϊκής Ένωσης στον εγγραματισμό σε θέματα Δεδομένων. Ενισχύοντας το επίπεδο εκπαίδευσης σε θέματα δεδομένων στην περιοχή, το έργο στοχεύει να ευθυγραμμίσει τα Δυτικά Βαλκάνια πιο στενά με τα σημεία αναφοράς της ΕΕ, διευκολύνοντας την ομαλή μετάβαση του εργατικού δυναμικού της στην αγορά εργασίας και την οικονομία της ΕΕ. Για να ανταποκριθεί σε αυτήν την πρόκληση, το έργο DELTA θα μεταφέρει τη γνώση από την ΕΕ και τρίτες χώρες που συνδέονται με το πρόγραμμα ορίζοντας το προφίλ του αλφαβητισμού δεδομένων για την τριτοβάθμια εκπαίδευση των Δυτικών Βαλκανίων, θα αναπτύξει μαθήματα δεδομένων που συμμορφώνονται με ένα σύστημα προσόντων που βασίζεται στις πιστωτικές μονάδες ECTS, θα εκπαιδεύσει τους εκπαιδευτές από τα ΑΕΙ των χωρών στόχου και θα διεξάγει πιλότο στα ιδρύματα αυτά. Παράλληλα, οι εταίροι θα αναπτύξουν ένα ψηφιακό περιβάλλον μάθησης για την ενσωμάτωση του βασισμένου στην ηλ-τάξη και ένα σύστημα επικύρωσης και πιστοποίησης.

Development of an antibody that specifically detects GHB is of paramount importance and a partner with expertise to the area of antibodies for small molecules will be employed (Abcalis GmbH). Nonetheless alternative binding molecules, such as aptamers, will be also explored. For the built up of the WLRS system the Institute of Nanoscience and Nanotechnology of NCSR “Demokritos” will collaborate with ThetaMetrisis S.A. to optimize the system for the requirements of the particular application especially those related to system portability and robustness. The Immunoassays/Immunosensors Lab of INRASTES, NCSR “Demokritos” will undertake the development of binding assays for detection of GHB with the optical methods and perform the initial evaluation of the three biosensing approaches. Electrochemical sensors will be fabricated by Jobst Technologies GmbH which will also develop and the measuring apparatus.

TRIFFID targets the development of an integrated approach for maximizing the efficiency of FR field operations, by advancing remote and smart robotic reconnaissance in disaster sites, while integrating it into European CP procedures.

GANDALF will set new grounds to the fight against advanced forms of cyber threats and cyber-dependent crimes; it will (i) realise cutting-edge agency-to-agency data sharing mechanisms, optimising the balance between transparency and privacy based on the needs of each LEA; (ii) deploy a modular, decision-support toolbox that will comprise not only ground-breaking technologies for collaborative investigation, prediction and identification of advanced forms of cyber threats, but also optimisation mechanisms enabling the full exploitation of these technologies; (iii) build on the above 2 offerings and deliver a customizable, collaborative crime investigation sandbox for scenarios, hypotheses analysis and crime investigation; and (iv) facilitate policy drafting, training and citizens’ engagement via innovative mechanisms that will realise the Cyber Hygiene 2.0 vision.

In the Optical and WIreless sensors Networks for 6G scenarios (OWIN6G) project, key industry and academic stakeholders are brought together with the aim of developing a structured European training programme (TP) in optical technologies to facilitate disruptive wireless sensor applications within sixth-generation (6G) networks. OWIN6G will be the first Doctoral Network dedicated to training new generation of early-stage researchers (i.e., doctoral candidates, DCs) in the field of wireless sensor networks (WSNs) for the Internet of Things/Internet of Everything as part of the 6G and beyond focusing on novel sensors, solar-cells for energy harvesting and optical detection, and hybrid RF-optical wireless technologies, and the application of machine learning to improve adoption, optimization, and security aspects in sensor networks. OWIN6G will contribute significantly to the fundamental scientific understanding, technical know-hows and innovation of the future hybrid RF/optical WSN through the collaborative research involving ten individual DCs projects addressing specific challenges and applications. OWIN6G will make significant contributions to the fundamental scientific understanding, technical know-how and innovation of the future hybrid optical/RF sensor network. In addition to technical TP through PhD courses, dedicated tutorials and workshops organized by the Doctoral Network, DCs will be offered complementary non-technical training activities, including entrepreneurship, authoring scientific papers/patents, dissemination, etc. Having industrial partners participate will further enhance DC's technological progress by focusing on standardization, commercialization, and handling of real-world projects in a real-world environment.

Despite the multiple advantages of products remanufacturing, being widely recognised as an effective means for transitioning to a more circular economy, there is still need for improved research and experimental observations, to improve traceability and reliability of the final products from end-users’ perspectives, as well as enhanced impacts monitoring. The primary R3-Mydas objective is to develop a multi-actor framework, integrating innovative digital technologies (ML for process and quality control, marketplace, graph models for defects detection, digital twins), advanced mechatronics (AM, laser-cladding, automated disassembly/reassembly) and newly developed approaches from SSH (extended TAM/UTAUT models, ethics and legal framework), for functionally, environmentally and economically sustainable circular value chains for remanufacturing of energy goods at the factory level (Oil&Gas crankshafts – demo 1, E-vehicles batteries – demo 2, Wind turbines gearboxes – demo 3). R3-Mydas will deliver unprecedented impacts throughout the targeted value chains, as follows: up to 60% time reduction in programming for remanufacturing; up to 20% increased product quality; up to 30% rework reduction [Demo 1]; up to 30% improved detection of tiny deviations from normal behaviour; up to 50% faster anomaly localization; up to 30% increase the number of different modality data streams handled; up to 20% faster fusion process [Demo 2]; up to 99% reusage rate; -90% prevention rate; -75% lead time; up to 85% raw material savings potential [Demo 3]. The Project will deliver a marketplace associating to each remanufactured product or services/component for remanufacturing a Digital Passport-like set of information, ensuring full traceability. Finally, a dedicated training programme will be designed and delivered by EITM, targeting the Project remanufacturing value chains (100+ training hours and 100+ diverse stakeholders engaged during the Project).

UNDERPIN addresses the vision of European Digital Single Market with the idea of a European-wide Data Space for Manufacturing for dynamic asset management and predictive/prescriptive maintenance. European manufacturers in the refinery and renewable energy domains, their value chain ecosystem of SMEs and governmental, research and civil society stakeholders will be equipped with sustainable and secure yet user friendly Data Space aiming at improving processes, cost structure and material management via beyond the state-of-the-art data analysis. The solution is compliant to EU standards, Data acts, IDSA and GAIA-X guidelines encompassing enterprise-ready infrastructure and tools to accommodate secure and trusted data, improving company operations. UNDERPIN Data Space will provide a cross-organizational and cross-use-case data sharing and exchanging, a solution that ensures data sovereignty, with strong focus on the interplay of SMEs and large industry players to improve products and services. Real world demonstrators in the demanding and diverse oil refinery and wind farm domains will be implemented. UNDERPIN will ensure higher performance, better insight on asset critically, reduced overall downtimes and maintenance costs, extended machine usage periods, improved future machine designs, new service models, improve production line operations, company-internal processes, increased usability and enhance business opportunities for industrial data value added services, supporting the transition towards circular economy. UNDERPIN will make use of existing embryonic data space of the lead partner Motor Oil. This will be transferred to existing mature Data Space technology to ensure security, interoperability, scalability, high performance, increased usability, standards compliance. UNDERPIN develops a trust framework, a sustainable business model for the Data Space operator Motor Oil, and implements a Europe-wide scale-out plan to establish a European Data Space for Manufacturing.

Συνέχιση/επανέναρξη υλοποίησης ενός επιπλέον πακέτου εργασίας στα πλαίσια CCN (Contract Change Notice). «SeasFire: Earth System Deep Learning for Seasonal Fire Forecasting in Europe»

FOSSBot is a project with enormous potential to revolutionize STEM education by introducing students to robotics and programming in an accessible and affordable way. In terms of the project FOSSBot will become multilingual, will be expanded with a few more sensors and will be enriched with educational material and supporting activities. It will also be ported in a simulation environment (such as CoppeliaSim or Gazebo) in order to support the quick onboarding of educators and students. The educational material and activities developed for FOSSbot will provide a structured approach to learning, making it easier for educators to incorporate robot-based activities and programming concepts into their curriculum. The material will be designed to be accessible to students with different skill levels, allowing them to progress at their own pace. The activities will be designed to be engaging and interactive, encouraging students to explore and experiment with the concepts they are learning.

Answer set programming (ASP) is a declarative logic programming paradigm for solving difficult computational problems. Nowadays, ASP is considered as one of the most successful formalisms of logic-based AI, having been used in many industrial-level problems. The most difficult foundational problem in ASP, is its extension in order to handle more demanding computational tasks. Any significant extension to the expressive power of ASP that is also easy to use by programmers, will be a major breakthrough. To tackle this problem, we propose to extend ASP in two significant and orthogonal ways: to extend the underlying language from first-order to higher-order logic and to allow preferential logical operators such as ordered disjunction in the logical rules of ASP. The key tasks of our proposal are to study the logical foundations, the expressive power, and the computational procedures of Higher-Order ASP and Preferential ASP. Overall, the proposed project will lead to a significant advancement of one of the most broadly used formalisms in modern logical-AI.

This project has the main goal to reduce E-waste in Europe in working on already existing E-waste, developing new tooling to extend lifetime and recyclability of E-waste, deploy at European level the new best practices and more important to develop a strong European ecosystem in this domain.

Artificial Intelligence in Manufacturing leading to Sustainability and Industry5.0

The mission of Elite4Mobility is to support the European automotive industry in transitioning from isolated and static products towards a service-centric, connected mobility ecosystem that will fully digitize the vehicle and share data across involved stakeholders. The project will enable and simplify development, deployment, operation and life cycle management for connected adaptive mobility solutions.

Climate change, CO2 footprint, energy transition, safety & security as well as sovereignty are key issues that the common population can very well relate to today. As we face critical challenges, research and development in our ECS domain needs to address them more than ever. In particular the energy transition needs to be accelerated in order to become more independent from gas and oil energy, which was considered being available at low costs and without limits until recently. This assumption has been proven to be based on very fragile grounds and it has come to an end. One solution to accelerate the energy transition is to use all generated energy. This means that overflow energy produced in wind or solar parks needs to be available in periods when common energy generation is lacking. This requires important investments into infrastructure, which will only flow, if investors trust into the technologies enabling solutions. The faster the trust is built, the faster the transition can be realized. On the other hand, technologies and products that will ensure an economic use of resources and enable long and trusted lifetime of systems and components in the ECS domain have to be developed. ARCHIMEDES contributes to this in the domains of automotive, aviation and industry. In ARCHIMEDES, components, models and methodologies to increase the efficiency and lifetime of the propulsion components, power components and energy storage devices in automotive, aviation and industry will be developed. This will support the energy transition on the consumer`s side. In order to support this mission, ARCHIMEDES aims to change technologies and products in the automotive, aviation, infrastructure domains and the related ecosystem towards a resilient, de-carbonized, digitalized, and green EU: It will help building trust in the new technologies and thus contribute to accelerating the energy transition, safety and security.

It aims at strengthening the recovery of the assets of high-level corruption and of sanctioned regimes and entities in the EU, through a combination of research, trainings and data driven tools for the public and private sector. In particular, it will achieve the following objectives: › Monitor the new forms of high-level corruption (including influence peddling) and disclose the transnational complex schemes which are employed by ‘kleptocrats’ to circumvent EU sanctions and investigations; › Improve the investigative and legal capabilities of EU public authorities in the corruption investigation/intelligence domain through dedicated trainings and e-learning materials; › Strengthen the capabilities of the private sector, and EU banks in particular, to detect transnational high-level corruption schemes as part of their AML/CFT activity; › Building on previous successful projects (first of all, DATACROS and EBOCS), develop a data-driven tool-box to boost the tracing of kleptocrats’ assets by providing EU public authorities integrated access to a variety of repositories (company data, beneficial ownership registers, real estate registries, vessel registers, sanction lists) and of data analytics services, including anomaly indicators; › Assess the limitations of the current EU sanction regime and make policy and legal recommendations to make it more effective against highly-corrupt entities and regimes.