Lessons learned from the RAICAM doctoral network research sprints and field demonstrations

Abstract

Doctoral Networks (DNs) aim to address systemic challenges in doctoral education, such as fostering interdisciplinarity, enabling international and intersectoral collaboration, enhancing employability, and promoting responsible innovation. While cohort-based training helps mitigate student isolation through workshops and summer schools, traditional DNs often struggle to fully realize their collaborative potential, often relying on predefined supervisor relationships or the initiative of individual researchers. In contrast, the Marie Skłodowska-Curie Actions (MSCA) Robotics and AI for Critical Asset Monitoring (RAICAM) DN was designed to maximize doctoral candidate (DC) collaboration and networking through a cohort-wide research challenge, requiring them to balance independent research with contributions to a shared, mission-driven objective. This study examines how structured training, including digital communities, application-focused research sprints, training schools, a robotics hackathon and a final demonstration enhances system integration and collaboration within the network. DCs located across seven European countries worked in virtual teams, refining systems through structured workflows, weekly meetings, and shared workspaces before training schools. Through continuous online collaboration and targeted sprints, RAICAM facilitated interdisciplinary integration. Two research sprints, conducted in Italy and France, and a robotics hackathon held in Austria, enabled teams to develop and test solutions for real-world challenges through an impact-driven plan that considers a given problem from an end-to-end perspective that requires and foster interdisciplinary collaboration. The results highlight the effectiveness of structured training in enhancing collaboration and adaptability, while identifying key areas for improvement. This study translates lessons from RAICAM into practical guidelines for future doctoral networks, demonstrating how structured training empowers students to drive interdisciplinary research independently.