A4 Refereed article in a conference publication
Lessons Learned from the RAICAM Doctoral Network Research Sprints
Authors: Kenan, Alperen; Kordkheili, Sahar Sadeghi; Garcia Cardenas, Juan Jose; Melone, Alessandro; Tian, Changda; Li, Haichuan; Raei, Hamidreza; Arachchige, Sasanka Kuruppu; Tang, Yifeng; Tapus, Adriana; Ollero, Anibal; Ajudani, Arash; Arrue, Begoña C.; Papageorgiou, Dimitrios; Kämäräinen, Joni-Kristian; Heikkonen, Jukka; Figueredo, Luis; Giuliani, Manuel; Trahanias, Panos; Bremner, Paul; Nekoo, Saeed Rafee; Watson, Simon; Westerlund, Tomi
Editors: Cavalcanti, Ana; Foster, Simon; Richardson, Robert
Conference name: Towards Autonomous Robotic Systems
Publisher: Springer Science and Business Media Deutschland GmbH
Publication year: 2025
Journal:: Lecture Notes in Computer Science
Book title : Towards Autonomous Robotic Systems: 26th Annual Conference, TAROS 2025, York, UK, August 20–22, 2025, Proceedings
Volume: 16045
First page : 526
Last page: 539
ISBN: 978-3-032-01485-6
eISBN: 978-3-032-01486-3
ISSN: 0302-9743
eISSN: 1611-3349
DOI: https://doi.org/10.1007/978-3-032-01486-3_40
Web address : https://doi.org/10.1007/978-3-032-01486-3_40
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 realise their collaborative potential, often relying on predefined supervisor relationships or the initiative of individual researchers. In contrast, Marie Skłodowska-Curie Doctoral Networks (MSCA-DNs) prioritise doctoral candidates (DCs), challenging them to balance independent research with contributions to a shared, mission-driven objective. This study examines how structured training, including digital communities and application-focused research sprints, enhances system integration and collaboration within the Robotics and AI for Critical Asset Monitoring (RAICAM) Doctoral 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, allowed teams to develop and test solutions for real-world challenges with an impact-driven plan that considers a given problem from and 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.
Funding information in the publication:
This work was supported by the European Commission’s Marie Skłodowska-Curie Action (MSCA) Project RAICAM (GA101072634) 2025.
