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 mobile robotics
Builds
Perception and decision-making layers for autonomous mobile platforms
Domain
Autonomous vehicles, mobile robotics, AI perception, sensor fusion
Deliverable
production ML models | research
Required skills
mobile robotics, autonomous systems, AI perception, sensor fusion, probabilistic modeling, uncertainty quantification, simulation, embedded systems
Preferred skills
ADAS/AD, vehicle architecture, functional safety, system engineering, dynamic vehicle state estimation
Technologies
Reinforcement Learning, VLA, imitation learning, LiDAR, radar, cameras, digital twin
Responsibilities
Conduct literature review on unmanned vehicle traversability and perception; develop probabilistic traversability estimation modules; study advanced AI models for terrain qualification; integrate vehicle state indicators (slip, adhesion, dynamic margin); define confidence metrics and safety-compliant decision thresholds; validate approaches via simulation, digital twins, and prototype experiments; contribute to scientific publications and industrial knowledge capitalization.
Seniority
PhD candidate, research-focused IC
