Thèse CIFRE pour Estimation Probabiliste De Franchibilité Pour Des Plateformes Terrestres Non Habitées Résilientes En Environnements Non Structurés. ( H/F)
Core
Developing probabilistic terrain traversability estimation for autonomous unmanned ground vehicles in unstructured environments using AI-based perception and sensor fusion.
Role type
Research PhD (CIFRE) in autonomous systems and probabilistic robotics
Builds
Perception and estimation layers for autonomous mobile platforms
Domain
Autonomous vehicles, robotics, AI perception, sensor fusion
Deliverable
production ML models
Required skills
probabilistic modeling, sensor fusion, autonomous systems, mobile robotics, AI perception, uncertainty quantification
Preferred skills
reinforcement learning, VLA, imitation learning, system engineering, functional safety, embedded systems
Technologies
LiDAR, radar, cameras, simulation, digital twins
Responsibilities
conduct state-of-the-art review on unmanned vehicle traversability and perception; develop a probabilistic traversability estimation module; study advanced AI models for terrain qualification; integrate vehicle state indicators (slip, traction, dynamic margin); define confidence metrics and decision thresholds compatible with safety assurance; validate approaches via simulation, digital twins, and prototype experiments; contribute to scientific publications and thesis deliverables.
