Doctorant PhD Thesis CIFRE "Architectures de fusion multimodale spatio-temporelle pour la perception automobile robuste appliquée aux cas d’usage NCAP et ADAS de niveau L2/L2+/L2++" (H/F)

Tasks

• Analyze sensor asynchronism and temporal dimension handling
• Conduct literature review on multimodal sensor fusion architectures
• Design multimodal spatiotemporal perception architectures
• Develop perception architectures using camera radar and vehicle sensors
• Evaluate solutions for ADAS scenarios with robustness and latency constraints
• Publish research and develop demonstrators and patentable innovations
• Train deep learning models on real world datasets

Perks/Benefits

• N/A

Skills/Tech-stack

C# | C++ | Computer Vision | Data Science | Deep learning | Foundation Models | Git | Linux | Machine Learning | Multimodal Perception | PyTorch | Python | Self-Supervised Learning | Self-supervised | Sensor fusion | Spatiotemporal modeling | Supervised Learning | TensorFlow | Transformers