2027届-控制工程师-Control Engineer

该职位来源于猎聘 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:

• 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.
• 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.
• Deploy control systems in real-time equipment systems, conduct on-site testing, verification, and commissioning work, and ensure the successful delivery of control systems.
• Construct scheduling models and decision optimization algorithms based on discrete events and actual data to help improve production capacity and optimize resource allocation.
• 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:
• Doctoral/Master's degree in Control Science and Engineering, Mechanical Engineering, Electrical Engineering, Industrial Engineering, Artificial Intelligence (AI), or other related fields.
• 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.
• 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.).
• Proficient in at least one programming language among Matlab/Simulink, Python, C/C++, R, and Java; experience in Web UI development is preferred.
• 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.
• Excellent verbal and written communication skills; experience in interdisciplinary collaboration or publication of relevant technical papers is preferred.