中微半导体设备（上海）股份有限公司

岗位职责： 1.运用经典 / 现代控制理论、统计机器学习、运筹优化理论、物联网技术等，开发并落地系统建模、先进/智能过程控制、优化问题求解、离散事件仿真、调度优化、路径规划、边缘计算部署等技术方案，为半导体设备的控制开发与决策优化提供支撑。 2.推动基于模型与学习的控制设计，支撑中微全系列产品的设计开发，系统性提升产品控制性能。 3.在设备实时系统中部署控制系统，开展现场测试、验证与调试工作，确保控制系统顺利交付。 4.基于离散事件与实际数据，构建调度模型与决策优化算法，助力产能提升与资源优化。 5.推动人工智能技术在联合仿真、过程控制、决策优化等场景的落地应用。 Job Responsibilities: 1.Utilize classical/modern control theory, statistical machine learning, operations research and optimization theory, Internet of Things (IoT) technology, etc., to develop and implement technical solutions such as system modeling, advanced/intelligent process control, optimization problem solving, discrete event simulation, scheduling optimization, path planning, and edge computing deployment, providing support for the control development and decision optimization of semiconductor equipment. 2.Promote model-based and learning-based control design, support the design and development of all AMEC's product lines, and systematically improve product control performance. 3.Deploy control systems in real-time equipment systems, conduct on-site testing, verification, and commissioning work, and ensure the successful delivery of control systems. 4.Construct scheduling models and decision optimization algorithms based on discrete events and actual data to help improve production capacity and optimize resource allocation. 5.Promote the application and implementation of artificial intelligence (AI) technology in scenarios such as co-simulation, process control, and decision optimization. 岗位要求： 1.博士/硕士学历，控制科学与工程、机械工程、电气工程、工业工程、人工智能等相关专业背景。 2.具备动态系统建模及控制系统设计、分析、仿真与实现能力；或掌握边缘AI、运筹优化、离散事件仿真、资源配置优化、路径规划、ROS 开发相关基础。 3.掌握经典/现代控制理论，修读过线性代数、优化控制、鲁棒控制、基于学习的控制等相关课程（如 PID、ADRC、MPC、H - 无穷、ILC、RLC）；或精通运筹优化理论（如凸优化、MIP、GA、PSO、RL、列生成等）。 4.精通 Matlab/Simulink、Python、C/C++、R、Java 中至少一种编程语言，具备 Web 用户界面开发经验者优先。 5.具备基于模型的控制设计、嵌入式系统开发、半导体调度系统开发，或 AI 在控制、调度、路径规划等领域的应用经验者优先。 6.具备良好的口头与书面沟通能力，有跨学科合作经验或相关技术论文发表者优先。 Job Requirements: 1.Doctoral/Master's degree in Control Science and Engineering, Mechanical Engineering, Electrical Engineering, Industrial Engineering, Artificial Intelligence (AI), or other related fields. 2.Possess capabilities in dynamic system modeling, as well as control system design, analysis, simulation, and implementation; or have relevant foundations in operations research and optimization, discrete event simulation, resource allocation optimization, path planning and obstacle avoidance, and ROS development. 3.Master classical/modern control theory, and have completed relevant courses such as Linear Algebra, Optimal Control, Robust Control, and Learning-Based Control (e.g., PID, ADRC, MPC, H-infinity, ILC, RLC); or be proficient in operations research and optimization theory (e.g., convex optimization, MIP, GA, PSO, RL, column generation, etc.). 4.Proficient in at least one programming language among Matlab/Simulink, Python, C/C++, R, and Java; experience in Web UI development is preferred. 5.Prior experience in model-based control design, embedded system development, semiconductor scheduling system development, or the application of AI in control, scheduling, path planning, and other related fields is preferred. 6.Excellent verbal and written communication skills; experience in interdisciplinary collaboration or publication of relevant technical papers is preferred.