The continuously growing need for higher data-rates and, therefore, more signal bandwidth in wireless communications, requires the use of multi-antenna base stations employing the recently introduced massive Multiple-Input-Multiple-Output (MIMO) concept and operating at millimeter-wave frequencies, e.g. 30 GHz. However, the implementation of such complex antenna systems into highly-integrated, energy- and cost-effective solutions is very challenging. Therefore, we propose an innovative antenna system concept utilizing silicon semiconductor electronics that can generate or receive at millimeter-wave frequencies in order to truly expand wireless communications into the outer limits of radio technology. SILIKA establishes a training network with leading R&D labs from European industries, universities and technology institutes in the domain of wireless infrastructure. This will be achieved by a multi-disciplinary approach combining expertise in all required areas to create a breakthrough towards millimeter-wave multi-antenna systems for energy-efficient and low-cost base stations for 5G wireless infrastructure. In the SILIKA Graduate School we will train 12 ESRs with post-master level technical courses and industrial workshops which are complemented by several professional-skill training modules relevant for working in multi-disciplinary project teams. All ESRs will perform secondments in an industrial setting. The SILIKA consortium consists of key European players in the field of wireless infrastructure with a complementary field of expertise and with a proven track-record in joint collaborations. As a consequence, SILIKA will provide the ESRs with a comprehensive set of transferable skills relevant for innovation and long-term employability. The high level of participation of leading industries will ensure that the scientific results of SILIKA will be transferred to future products in the area of wireless infrastructure which will benefit the European economy.

Embedded Artificial Intelligence (AI) has emerged as a transformative technology with immense potential to revolutionise various domains, spanning from robotics and healthcare to environmental monitoring and the Internet of Things. This Doctoral Network (DN) project ANT aims to train a network of 15 excellent Doctoral Candidates (DCs) by addressing the fundamental challenges of Embedded AI and accelerating the development of Embedded AI systems and applications through an innovative and interdisciplinary research and training program. ANT consists of four interconnected Work Packages (WPs) that encompass different aspects of Embedded AI. WP1 tackles the challenges in designing low-footprint standalone Embedded AI models under resource constraints and with diverse contexts and evolving environments. WP2 goes beyond standalone Embedded AI and designs innovative distributed and scalable learning solutions for heterogeneous Embedded AI networks under energy and bandwidth constraints. WP3 enhances the trustworthiness of Embedded AI with explainability, robustness, security, and privacy. ANT concludes in WP4 with a concerted effort to transfer fundamental research contributions to industry-relevant applications in autonomous robotics, underwater IoT, mobile healthcare, and smart farming, boosting Europe’s position in the global AI market both from a talent and a technological perspective. These interdisciplinary and inter-domain research training, along with the comprehensive soft-skills training (spanning from presentation skills to intellectual property, marketing, and economics, etc.) will make ANT’s 15 DCs highly employable in various industries, academia, or public government bodies, and will position the EU at the forefront of the emerging revolution of Embedded AI on Things.