Q-FENCE is a transformative initiative aimed at securing tomorrow’s digital infrastructure with quantum-resistant cryptography. In response to the rising threats posed by quantum computing, Q-FENCE develops a robust hybrid framework integrating classical, quantum, and post-quantum cryptographic techniques. Utilizing innovative approaches such as Ring-LWE, Module-LWE, Quantum Random Number Generators, and hardware-accelerated primitives, the project establishes a dual-layer security model that fortifies data protection across diverse infrastructures. Leveraging hardware-accelerated primitives and energy-efficient protocols, Q-FENCE ensures quantum resilience while addressing critical challenges such as seamless integration with legacy systems, regulatory compliance, and scalable deployment. By addressing key challenges like legacy system integration, interoperability, and regulatory compliance, it ensures a phased and seamless adoption across sectors including finance, healthcare, IoT, satellite and digital networks. Through sector-specific demonstrators, best-practice guidelines, and collaboration with EU policymakers, Q-FENCE bridges the gap between theoretical advancements and real-world applications, fostering digital sovereignty and resilience. Positioned as a critical player in the quantum-ready future, Q-FENCE not only safeguards sensitive data but also empowers stakeholders with practical, scalable, and innovative post-quantum solutions.

AID4SME will facilitate SMEs in developing combined AI and data solutions for large scale resource optimisation challenges for industries that have significant impact on the objectives of the Green Deal. Minimum 20 SMEs, selected through 2 open calls, will receive FSTP to develop these solutions with the support of a Community of Practice (COP). The ambition is to create a COP that will continue after the project lifetime. AID4SME brings together 9 technology blocks and low-TRL playgrounds from 4 scientific partners, to educate and support the SMEs. Additionally, 4 large industry partners (from automotive, whitegoods, battery and energy sector) provide real-life large scale resource optimization challenges that require combined AI and data solutions, and high-TRL playgrounds to integrate and demonstrate the solutions.AID4SME offers an open platform that is flexible to bring in challenges from outside the consortium. AID4SME provides the infrastructure and learning environment that enable the SMEs to solve the challenges, demonstrate solutions and grow into impactful enterprises. The technology blocks cover a wide area of AI & data technologies for the full cycle of data collection, creation of insights, decision support and automation. These technologies have the potential to have significant impact on the Green Transition and boost EU competitiveness for industries. AID4SME will collaborate with the AI-on-Demand platform to enrich its repository with the AID4SME tools and framework, while it is open to deploy the tools/frameworks already available on the AI-on-Demand platform for new use cases. AID4SME will assess the impact of the developed technologies on Green Deal objectives and on social and human aspects. AID4SME brings along partners who are experienced in re-skilling and up-skilling of SMEs and applying standardization as enabler for exploitation. The wide geographical coverage, with partners and DIHs from all across Europe, ensures maximum impact.

Electrification 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.