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Open nowPosted 18 days ago

强化学习工程师

wcn7iq2vq1am/86371187 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now强化学习工程师wcn7iq2vq1am/863711 · Shanghai, Chinese Mainland
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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. wcn7iq2vq1am/863711 postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 58.1%60 days
This job: posted 18 days ago

wcn7iq2vq1am/863711 median: 1 days open

The posting

1. 负责机器人操作任务的强化学习环境建模与仿真系统设计,构建高效、逼真的物理仿真环境; 2. 研发适用于机器人实际任务的强化学习与模仿学习算法,提升策略在真实场景中的泛化性、鲁棒性和样本效率; 3. 针对机器人实际控制问题,改进和优化深度强化学习算法(如PPO、SAC、TDMPC、RLPD 等),并结合模仿学习等方法提升训练效果; 4. 推动仿真到真实(Sim2Real)的迁移,设计域随机化、系统辨识、自适应控制等方法,降低仿真与实际的差异; 5. 与硬件、控制、感知团队紧密协作,将算法部署至真实机器人系统,并进行系统级验证与迭代优化。

1. 计算机、自动化、机器人、电子工程、人工智能等相关专业硕士及以上学历,具备扎实的数学与算法基础;(特别优异的本科生也可以考虑) 2. 熟悉常用强化学习算法(如PPO、SAC、TD3、TDMPC、RLPD等),具备扎实的算法实现和调优经验,熟练掌握PyTorch、JAX等至少一种深度学习框架; 3. 具备机器人学基础,熟悉运动学、动力学建模,有实际机器人系统(机械臂、移动机器人等)算法开发或部署经验者优先; 4. 在强化学习、机器人相关顶会(NeurIPS,ICML,ICLR,RSS,CoRL等)发表论文或相关顶会(NeurIPS,ICML,ICLR,RSS,CoRL等)发表论文或有强化学习真机部署经验优先; 5. 具备良好的团队协作和沟通能力,对机器人技术有强烈兴趣和自驱力。

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