智能驾驶强化学习算法科学家_CR

Job description

- 针对端到端和 VLA 自动驾驶模型，开展强化学习算法研究与创新。 - 探索先进的强化学习方法（如策略优化、在离线 RL、层次化 RL、多智能体 RL），提升算法的鲁棒性和泛化能力。 - 探索先进的强化学习方法在自动驾驶领域的应用和拓展。 - 设计评测基准，并在复杂动态驾驶场景中开展实验验证。 - 与感知、规划、仿真团队协作，将 RL 方法集成到端到端自动驾驶训练框架中。 - 形成研究成果，撰写技术文档或对外发表技术报告。 - Research and develop novel reinforcement learning algorithms for end-to-end autonomous driving and VLA (Vision-Language-Action) models. - Explore advanced RL techniques (e.g., policy optimization, online/offline RL, hierarchical RL, multi-agent RL) to improve robustness and generalization. - Familiar advanced RL algorithm(e.g. GRPO, GSPO etc.) to improve autonomous driving. - Design benchmarks and conduct experiments to evaluate algorithm performance in dynamic driving environments. - Collaborate with perception, planning, and simulation teams to integrate RL methods into E2E autonomous driving pipelines. - Publish technical reports and document research findings. 1. 计算机、机器学习、自动化、机器人等相关专业硕士或博士学历。 2. 深入理解强化学习、深度学习与控制理论。 3. 熟悉 PyTorch/TensorFlow 及常用 RL 框架（如 RLlib、Stable-Baselines3）。 4. 在自动驾驶、机器人或多智能体系统方向有研究经验者优先。 5. 具备扎实的 Python/C++ 编程能力，熟悉大规模训练。 6. 具备较强的分析与问题解决能力，能够独立开展研究。 7. 具备良好的英文读写能力。 1. Ph.D. degree in Computer Science, Machine Learning, Robotics, or related fields. 2. Strong knowledge of reinforcement learning, deep learning, and control theory. 3. Hands-on experience with PyTorch/TensorFlow and common RL libraries (e.g., RLlib, Stable-Baselines3, etc.). 4. Research background in autonomous driving, robotics, or multi-agent systems is highly preferred. 5. Solid programming skills in Python/C++, good understanding of large-scale training. 6. Strong analytical and problem-solving skills; ability to work independently. 7. English reading/writing proficiency. Do you want beneficial technologies being shaped by your ideas? Whether in the areas of mobility solutions, consumer goods, industrial technology or energy and building technology - with us, you will have the chance to improve quality of life all across the globe. Welcome to Bosch.