A European doctoral network will train 15 researchers to build artificial intelligence that runs on small, low-power devices—not in the cloud. Embedded AI—AI that operates inside a robot, a sensor, or a wearable—faces fundamental constraints. Chips have limited memory and energy, environments change, and networks of devices must share data without overwhelming bandwidth. Current AI models are often too large, too centralised, or too opaque to work reliably under these conditions. This project addresses that gap by training a cohort of doctoral candidates to design AI that is compact, distributed, and trustworthy. If successful, the research could make autonomous robots, underwater Internet-of-Things sensors, mobile health monitors, and smart farming equipment more capable and efficient. These systems quietly underpin logistics, environmental monitoring, and healthcare delivery. The project also aims to boost Europe’s talent pipeline and competitive position in the global AI market. The training includes soft skills—presentation, intellectual property, marketing—so graduates are employable across industry, academia, and government.
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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.
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