岗位职责： 与工艺、设备、控制、仿真及软件工程师紧密协作，面向半导体装备中的复杂工程问题，开展人工智能与物理机理融合研究，形成具有创新性和产业价值的技术解决方案。根据专业背景，重点承担以下一个或多个方向： 1.开展半导体装备大数据平台建设，研究多源异构数据治理、分析与智能应用，构建AI-ready数据底座 2.开展设备故障检测与分类、根因分析、腔室匹配分析、故障机理分析、健康评估、预测性维修等研究，提升设备稳定性与智能运维能力 3.开展针对SEM/TEM的智能量测、三维重建、面向腔体状况的视觉监控等工程落地以及计算机视觉领域前沿技术研究 4.开展机理数据双驱动的实验设计、虚拟量测，虚拟工艺预测、及多目标工艺优化研究，提升工艺开发效率与性能 5.研究AI与多物理场仿真融合技术，包括代理模型、物理约束机器学习、神经算子等，实现复杂物理过程的快速预测与优化。 6.探索大模型、智能体、世界模型及Physical AI等前沿技术，推动半导体装备向自主感知、推理、决策与控制演进 Collaborate closely with process, TPS, control, simulation, and software engineers to address complex engineering challenges in semiconductor equipment. Conduct research integrating artificial intelligence with physical mechanisms to form innovative and industry-valuable technical solutions. Depending on professional background, primarily undertake one or more of the following directions: 1.Conduct the construction of big data platforms for semiconductor equipment; research multi-source heterogeneous data governance, analysis, and intelligent applications; and build an AI-ready data foundation. 2.Conduct research on fault detection and classification (FDC), root cause analysis, chamber matching analysis, failure mechanism analysis, health assessment, and predictive maintenance to enhance equipment stability and intelligent operation and maintenance capabilities. 3.Conduct engineering implementation and frontier computer vision research in intelligent metrology for SEM/TEM, 3D reconstruction, and visual monitoring oriented toward chamber conditions. 4.Conduct research on hybrid mechanism- and data-driven design of experiments (DoE), virtual metrology, virtual process prediction, and multi-objective process optimization to improve process development efficiency and performance. 5.Research the integration technologies of AI and multi-physics simulation, including surrogate models, physics-informed machine learning, and neural operators, to achieve rapid prediction and optimization of complex physical processes. 6.Explore frontier technologies such as large models, intelligent agents, world models, and Physical AI to drive the evolution of semiconductor equipment toward autonomous perception, reasoning, decision-making, and control. 岗位要求： 1.人工智能、计算机、自动化、软件工程、电信信息、应用数学、物理、机械等相关专业，硕士及以上学历 2.具备扎实的数学、机器学习、深度学习、优化算法或科学计算基础，能够独立开展算法研究与模型开发 3.熟练掌握 Python，熟悉 PyTorch、TensorFlow、Scikit-learn 等主流AI与数据分析工具；具备基于大模型进行代码生成、调试、重构与快速原型开发的能力 4.在工业AI、时序建模、智能控制、工艺优化、AI for Science、多物理场仿真、大模型或智能体等方向具有相关研究或项目经验 5. 具备较强的问题分析、技术创新与工程实践能力，能够将复杂工程问题转化为可验证、可落地的AI解决方案 6. 具有高水平论文、专利、算法竞赛成果或大型工业项目落地经验者优先。