A European doctoral network will train 15 researchers to build artificial intelligence that runs directly on small devices—sensors, robots, medical monitors—rather than relying on cloud servers. Today’s AI typically requires powerful data centres to process information, which creates problems for devices that have limited battery, memory, or internet access. A farming sensor in a remote field, a wearable heart monitor, or an underwater drone cannot constantly stream data to the cloud. This project tackles the fundamental engineering challenges of making AI models small enough, efficient enough, and trustworthy enough to operate independently on such devices. If the research succeeds, it could enable practical AI applications in autonomous robots, underwater Internet-of-Things networks, mobile healthcare devices, and smart farming systems. The work also addresses reliability and privacy: embedded AI that explains its decisions, resists hacking, and protects user data. Beyond the technology, the network aims to boost Europe’s competitive position in the global AI market by producing 15 highly skilled graduates trained in both technical and commercial skills.
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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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