SymAware addresses the fundamental need for a new conceptual framework for awareness in multi-agent systems (MASs) that is compatible with the internal models and specifications of robotic agents and that enables safe simultaneous operation of collaborating autonomous agents and humans. The goal of SymAware is to provide a comprehensive framework for situational awareness to support sustainable autonomy via agents that actively perceive risks and collaborate with other robots and humans to improve their awareness and understanding, while fulfilling complex and dynamically changing tasks. The SymAware framework will use compositional logic, symbolic computations, formal reasoning, and uncertainty quantification to characterise and support situational awareness of MAS in its various dimensions, sustaining awareness by learning in social contexts, quantifying risks based on limited knowledge, and formulating risk-aware negotiation of task distributions. These objectives will be achieved in SymAware through (a) logical characterisation of awareness using symbolic methods, (b) quantifying the symbolic reasoning for awareness with spatial and temporal ingredients for decision making, (c) risk awareness via quantified knowledge, (d) quantifying and communicating knowledge awareness, (e) demonstrating awareness engineering in aviation and automotive use cases, and (f) identifying requirements for ethical and trustworthy awareness in human-agent interaction. The objectives of SymAware address the "Awareness Inside" Challenge of EIC by extending and formalising human-based models of situational awareness and by providing a novel conceptual situational awareness framework for MASs that encompasses logical characterisation and integrative formal reasoning of interdependent awareness dimensions including knowledge, spatiotemporal, risk and social dimensions. This will support transitioning to safe mixed operation of autonomous agents and humans.

Organic Bioelectronics is a fast-rising field encompassing organic electronic devices that exhibit mixed electronic and ionic conductivity. It represents a truly unique communication bridge across the technology gap existing between the living systems and digital electronics. Biosensing is one of the most scientifically and industrially promising application of organic (bio)electronics. It is important to form young professionals that will be able to operate into this highly-interdisciplinary field, where proficiency in chemistry, materials science and technology, solid state physics, biochemistry, engineering is needed. Such curricula can be hardly constructed within institutional degrees, at least not at the level that can be provided by a European Training Network. The objective of BORGES is to train the next generation of R&D innovators in organic bioelectronics, with the aim of developing organic biosensors up to demonstration in an end-user significant context. BORGES trainees will be educated with a holistic perspective of the technology, from fundamentals and fabrication, through characterization, to clinical/research user scenarios. BORGES training will be based on i) acquiring solid background in different scientific and technological fields; ii) exposing trainees to diverse sectors, from academia to technological research centres to industrial nodes; iii) fostering the development of transversal competencies. The BORGES Network is composed by 12 beneficiary institutions and one associate partner. With 4 non-academic nodes, and research centres with a clear industrial drive, BORGES ensures exposure of its fellows to a truly multisectorial environment. The state of the art training received by its fellows in a rapidly growing field with a strong socio-economic impact will fully qualify them to access novel and highly qualified job positions, and to substantially increase their employability and career perspectives.

Connected and Cooperative Automotive Mobility (CCAM) solutions have emerged thanks to novel Artificial Intelligence (AI) which can be trained with huge amounts of data to produce driving functions with better-than-human performance under certain conditions. The race on AI keeps on building HW/SW frameworks to manage and process even larger real and synthetic datasets to train increasingly accurate AI models. However, AI remains largely unexplored with respect to explainability (interpretability of model functioning), privacy preservation (exposure of sensitive data), ethics (bias and wanted/unwanted behaviour), and accountability (responsibilities of AI outputs). These features will establish the basis of trustworthy AI, as a novel paradigm to fully understand and trust AI in operation, while using it at its full capabilities for the benefit of society. AITHENA will contribute to build Explainable AI (XAI) in CCAM development and testing frameworks, researching three main AI pillars: data (real/synthetic data management), models (data fusion, hybrid AI approaches), and testing (physical/virtual XiL set-ups with scalable MLOps). A human-centric methodology will be created to derive trustworthy AI dimensions from user identified group needs in CCAM applications. AITHENA will innovate proposing a set of Key Performance Indicators (KPI) on XAI, and an analysis to explore trade-offs between these dimensions. Demonstrators will show the AITHENA methodology in four critical use cases: perception (what does the AI perceive, and why), situational awareness (what is the AI understanding about the current driving environment, including the driver state), decision (why a certain decision is taken), and traffic management (how transport-level applications interoperate with AI-enabled systems operating at vehicle-level). Created data and tools will be made available via European data sharing initiatives (OpenData and OpenTools) to foster research on trustworthy AI for CCAM.

VIMMP facilitates and promotes the exchange between all materials modelling stakeholders for the benefit of increased innovation in European manufacturing industry. VIMMP will establish an open-source, user-friendly, powerful web-based marketplace linking beneficiaries from different manufacturing industry sectors with relevant materials modelling activities and resources. To enable a seamless and fully integrated environment, VIMMP is built on solid taxonomy and metadata foundations, including those centred on materials models, software tools, communities, translation expertise and training materials. VIMMP is a true marketplace, offering a substantial boost to all providers of tools and services; integrating modelling platforms based on Open Simulation Platform (OSP) standards that will be pursued in collaboration with the EMMC. Thus, any software owner can easily integrate models and certify codes to adhere to OSP standards. The Translator function will be supported by novel, collaborative tools that use metadata to combine models on an abstract logical level. OSP standards enable Translators and End User to build and deploy workflows quickly. VIMMP contributes novel avenues for coupling and linking of models, which will be validated in the context of three overlapping industry applications: personal goods, polymer nanocomposites and functional coatings. Data repositories relevant to modelling will be developed and integrated in VIMMP, including a novel input parameter repository for mesoscopic model, also materials properties and associated validation data. VIMMP will comprise a full set of education and training resources relevant for a wider range of manufacturing industry. VIMMP users will profit from lowering risk and upfront cost, greater speed and agility of deploying materials modelling and realising the wide range of demonstrated economic impacts.

Safety assurance of Cooperative, Connected, and Automated Mobility (CCAM) technologies and systems is a crucial factor for their successful adoption in society, yet it remains to be a significant challenge. CCAM must prove to be safe and reliable in every possible driving scenario. It is already acknowledged that for higher levels of automation the validation of these systems by real test-driving would be infeasible by conventional methods. Furthermore, certification initiatives worldwide struggle to define a harmonized approach to enable massive deployment of highly automated vehicles. Building from HEADSTART and other initiatives, SUNRISE (Safety assUraNce fRamework for connected, automated mobIlity SystEms) will develop and demonstrate a commonly accepted, extensible Safety Assurance Framework for the test and safety validation of a varied scope of CCAM systems. This will be achieved by: 1) Bringing the needs of heterogeneous CCAM use cases; 2) Defining a scenario-based database framework that will broaden the HEADSTART methodology; 3) Holistically addressing the CCAM test scenario generation; 4) preparing the required tools for comprehensive testing (virtual and physical), taking into account robustness, scalability, interoperability, quality and standardization; 5) integrating functional safety and cybersecurity; 6)involving the use cases from the initial stages, acting as a guiding principle within the project. The project will define, implement and demonstrate the building blocks of this Safety Assurance Framework: harmonized and scalable safety assessment methodologies, procedures and metrics taylored for use cases, a federated European Scenario Database framework and its necessary data interfaces, a commonly agreed simulation framework including tools and interfaces. SUNRISE will work closely with CCAM stakeholders as policy makers, regulators, consumer testing, user associations and all relevant stakeholders.