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Open nowPosted 40 days ago

具身智能高效推理与端侧部署科研实习生

sii-czxy/campus170 open roles

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Your applicationOpen now具身智能高效推理与端侧部署科研实习生sii-czxy/campus

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  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 40 days ago

The posting

1. 分析 VLA、WAM 等模型在连续机器人控制过程中的计算瓶颈，使用性能分析工具统计模型各模块的计算量、推理时延、显存占用和数据传输开销。 2. 复现并优化 FlashVLA 等方法，评估不同压缩策略对 FLOPs、端侧时延、内存占用和机器人任务成功率的影响。3. 3. 研究面向机器人控制的高效动作生成方法，探索控制相关 Token 选择、动态推理、动作缓存与复用、模型剪枝以及低比特量化等轻量化技术。 4. 参与模型在昇腾、Jetson 等端侧平台上的部署与适配，完成模型导出、算子兼容、精度校准、性能调优和端到端机器人推理流程验证。 5. 建立端侧具身智能评测流程，开展不同模型、硬件平台和压缩配置下的对比实验，整理部署文档、性能报告和可复现实验脚本。

1. 硕士研究生。 2. 计算机科学、人工智能、电子信息、自动化、机器人、计算机体系结构、嵌入式系统等相关专业优先。 3. 具备深度学习基础，了解神经网络推理过程，学习过计算机组成、并行计算、操作系统、嵌入式系统或高性能计算相关课程者优先。 4. 具备 Python 和 PyTorch 编程能力，熟悉 Linux 和 Git，能够有效利用 agent 工具，掌握 C/C++，或具有 CUDA、TensorRT、ONNX、CANN 等推理与部署工具使用经验者优先。 5. 了解模型量化、剪枝、稀疏化、Token 裁剪、缓存复用、混合精度推理或算子融合中的至少一种技术，能够使用性能分析工具定位计算和内存瓶颈。 6. 有 Jetson、昇腾、GPU 服务器、嵌入式 AI 平台或机器人计算平台部署经历者优先。 7. 具备较强的工程调试和问题定位能力，能够阅读英文技术文档，愿意处理软硬件兼容、环境配置和性能优化等实际问题，并能够稳定投入科研时间。 8. 线上线下结合，实习周期6个月以上，每周预计投入20-30小时。

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