Foundation Models for Autonomous Driving – Intern

Job Description

In this internship, some responsibilities will include: 

• Conduct advanced research and engineering in multimodal LLMs, 3D perception, knowledge distillation, and diffusion models to address challenges in autonomous systems.
• Apply research results to real-world applications with high quality implementation
• Integrate the resulting system/software into existing Bosch platform
• Summarize research findings in high-quality paper and/or patent submissions

Qualifications

Basic Qualifications

• Ph.D. student in Computer Science or related fields 
• Minimum 3.0 GPA
• Hands-on experience on developing computer vision and machine learning algorithms with focus on at least two of the following areas: multimodal foundation models, diffusion models, reinforcement learning detection/segmentation, 3D scene understanding, autonomous driving, and sensor fusion.
• Solid Python skills and proficient with libraries such as MMCV, and PyTorch.

Preferred Qualifications

• Publication record in top venues including CVPR, ICCV, ECCV, ICML, ICLR, and  NeurIPS.
• Familiar with SOTA vision and learning methods of multimodal LLMs, diffusion models, Diffusion Models, knowledge distillation, and so on.
• Able to work independently, has strong research and problem-solving skills
• Strong background in math and statistics is a plus.
• Good communication and teamwork skills