Requisition Number: 72822 The company built on breakthroughs. Join us. Corning is one of the world’s leading innovators in glass, ceramic, and materials science. From the depths of the ocean to the farthest reaches of space,
蔚来AGI超星计划-Agentic Semantic Search推荐投递圣何塞、上海、北京校招实习数字技术硕士及以上蔚来AGI超星计划职位 ID：A34979职位描述课题介绍About the positionJoin our AI Platform team to build intelligent agents that unlock the full value of our companys internal knowledge and data. You will work at the intersection of large language models,
Company Description About Averna A worldwide Test & Quality leader, Averna delivers innovative solutions for clients – including many Global 1000s – innumerousmanufacturing industries. What started as a three-person operation in 1999 is now a multinational
Company Description At WD, our vision is to power global innovation and push the boundaries of technology to make what you thought was once impossible, possible. At our core, WD is a company of problem solvers.
蔚来AGI超星计划-面向车身运动控制（VMC）的物理AI与多场景重建强化学习研究推荐投递上海校招实习数字技术硕士及以上蔚来AGI超星计划职位 ID：A109342职位描述课题介绍本课题面向车身运动控制（VMC）在复杂工况下的性能与安全问题，构建“参数辨识 + 物理仿真 + 强化学习 + 场景风险评估”的一体化控制框架。目标是在路面附着变化、载荷波动、非线性耦合和多执行器协同等条件下，实现更稳定、更可迁移的控制策略。相较传统“模型+规则+人工调参”方法，本课题重点研究可学习的补偿控制：在保留经典控制器主结构的基础上，引入前馈补偿与残差策略（residual policy），实现误差驱动的在线修正与实时优化。同时，课题建设场景驱动风险管控链路：围绕典型、极限、长尾和高风险场景，打通仿真、HIL 与实车验证流程，形成“场景生成—策略训练—风险评估—闭环验证”的完整体系，系统评估安全性、鲁棒性与泛化能力。具体工作1. 搭建多层级车身动力学仿真环境（简化模型到高保真模型）。2. 研究关键参数在线/离线辨识方法（轮胎、质量分布、阻尼、附着系数等）。3. 设计带物理与安全约束的强化学习策略，用于前馈补偿或残差控制。4. 研究误差学习模块与经典 VMC 控制器的协同机制。5. 构建 VMC 场景库与风险评估体系，支持风险分级和测试覆盖分析。6. 研究 Sim2Real 一致性与鲁棒性，建立基于实测反馈的持续优化机制。预期产出1. 研究报告或高水平论文（强化学习、智能控制、车辆动力学、安全验证等方向）。2. 一套面向 VMC 的学习控制框架：参数辨识、约束强化学习、风险评估、仿真闭环。3. 高价值场景库：覆盖典型、极限、长尾和高风险工况。4. 可在仿真与 HIL 验证、并具备实车迁移潜力的策略原型。5. 可复用的“仿真-HIL-实车”场景与泛化评测框架。6. 虚实闭环工具链：数据生成、场景挖掘、参数辨识、风险评估、策略优化。职位要求- 专业能力： - 具备强化学习、控制理论、车辆动力学或机器人学基础，熟悉PPO/SAC/Model-based RL等方法； -
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