Coordination adaptative de systèmes robotiques et systèmes connectés hétérogènes pour les transitions agroécologiques // Adaptive Coordination of Heterogeneous Robotic and Connected Systems for Agroecological Transitions

Cette thèse vise à développer des écosystèmes cyber-physiques intelligents capables d'adapter dynamiquement leurs stratégies de perception, de mobilité et d'intervention aux contraintes environnementales, climatiques et opérationnelles des systèmes agroécologiques. Les travaux porteront sur la coordination adaptative de robots hétérogènes, de capteurs connectés et d'infrastructures edge computing, ainsi que sur la modélisation multi-échelle des interactions entre dynamiques robotiques, agricoles et écologiques. Les données et comportements autonomes seront intégrés dans des cadres de simulation multi-agents et des jumeaux numériques basés sur la plateforme MAELIA afin d'évaluer les trajectoires de transition agroécologique et la résilience des systèmes autonomes en conditions non nominales.
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This PhD aims to develop intelligent cyber-physical ecosystems capable of dynamically adapting sensing, mobility, and intervention strategies to environmental, climatic, and operational constraints in agroecological systems. The research will focus on adaptive coordination of heterogeneous robots, connected sensors, and edge-computing infrastructures, as well as multi-scale modeling of interactions between robotic, agricultural, and ecological dynamics. Robotic data and autonomous behaviors will be integrated into multi-agent simulation frameworks and digital twins based on MAELIA to evaluate agroecological transition scenarios and the resilience of autonomous systems under non-nominal conditions.
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Début de la thèse : 01/10/2026

Other public funding

ANR Financement d'Agences de financement de la recherche

Université de Limoges

653 Sciences et Ingénierie

- Master's degree (or equivalent) in Robotics, Computer Science, Artificial Intelligence, Embedded Systems, or closely related fields - Strong background in at least several of the following domains is expected: - autonomous robotics and multi-agent systems, - IoT and embedded systems, - distributed systems and edge computing, - Good experience with C/C++, Python, ROS, and modeling and simulation tools - Good software engineering practices - Experience with data preprocessing, augmentation, and utilizing various AI frameworks - Experience with robotic middleware and software such as ROS/ROS2, real-time systems, or distributed robotic architectures will be appreciated - Excellent written English and communication skills - Scientific curiosity, autonomy, and the ability to work independently and collaboratively within a multidisciplinary research environment The project requires a strong interest in interdisciplinary research at the intersection of robotics, AI, distributed information systems, environmental sciences and sustainable systems. The candidate should be able to conduct both theoretical and applied research, including algorithm development, system integration, experimentation, and scientific evaluation.
- Master's degree (or equivalent) in Robotics, Computer Science, Artificial Intelligence, Embedded Systems, or closely related fields - Strong background in at least several of the following domains is expected: - autonomous robotics and multi-agent systems, - IoT and embedded systems, - distributed systems and edge computing, - Good experience with C/C++, Python, ROS, and modeling and simulation tools - Good software engineering practices - Experience with data preprocessing, augmentation, and utilizing various AI frameworks - Experience with robotic middleware and software such as ROS/ROS2, real-time systems, or distributed robotic architectures will be appreciated - Excellent written English and communication skills - Scientific curiosity, autonomy, and the ability to work independently and collaboratively within a multidisciplinary research environment The project requires a strong interest in interdisciplinary research at the intersection of robotics, AI, distributed information systems, environmental sciences and sustainable systems. The candidate should be able to conduct both theoretical and applied research, including algorithm development, system integration, experimentation, and scientific evaluation.