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Open nowPosted 214 days ago

实习-端到端大模型算法实习生-世界模型预研

nio/campus869 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now实习-端到端大模型算法实习生-世界模型预研nio/campus · Shanghai, Chinese Mainland

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The clock on this job

Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market. nio/campus postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.8%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 214 days ago

nio/campus median: 1 days open

The posting

设计并实现高性能高鲁棒性的端到端轨迹规划预测模型架构，应用部署到智能辅助驾驶系统当中。 深入研究面向智能辅助驾驶的端到端轨迹预测规划模型，调研业界最新科研进展，应用VLA, 强化学习, 扩散模型等技术，持续提升模型在各种复杂环境下的自主决策和规划能力，解决闭环和实车测试中的各种issue问题。 深入研究并改进端到端大模型的训练和评测流程，开发训练和评测基础设施，包括闭环训练，仿真评测等。 与感知、控制、多传感器融合、软件工程团队协作，将算法集成至车辆软件平台或仿真平台。

在读硕士或博士（计算机科学、电子工程、自动化、机器学习、机器人学等相关专业） 熟悉端到端模型的基本架构和训练方案，掌握轨迹规划和轨迹预测基本算法原理。 掌握强化学习（如PPO, GRPO算法）以及生成式算法（如自回归算法，扩散模型）的基本原理。 扎实的 Python 编程能力，能够熟练使用 PyTorch／TensorFlow 等深度学习框架。 扎实的论文调研阅读能力，能够跟踪业界的前沿进展并具备算法复现的能力。 对智能辅助驾驶系统有浓厚兴趣，具备良好的逻辑思维与算法背景。 加分项 有VLA, 强化学习具体项目经验者优先。 有使用扩散模型／生成模型（如 DDPM、Score Matching、Flow-matching）相关项目经验。 在顶会或顶级期刊（如 ICRA、ICCV、CVPR、NeurIPS、ICML等）发表过相关论文者优先。

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