Advanced Control Intern (电气方向)
招聘时间
即日起 至 2027-05-26
招聘单位
康宁
所在地区
武汉
工作地点
湖北省/武汉市/东西湖区 湖北省武汉市东西湖区柏泉街石湾北路1号
学历要求
硕士
实习时长
每周4天，每天4小时
职责描述
Scope of Position
• Seeking a highly capable Control (or Data Analysis) Engineer (intern) for advancing our understanding of the advanced glass manufacturing processes. Candidates must have a strong foundation in Advanced Control Theory and a high level of expertise in physical modeling. This position requires excellent communication and interpersonal skills for working with cross-functional global teams and stakeholders.
Education and Experience (minimum required for consideration)
• Ph.D. (preferred) or Master in Chemical, Mechanical, Software, Computer Science or Electrical Engineering discipline
• Direct experience with data analysis, AI/ML, advanced controls technologies and/or first-principles/physics-based modeling
Required Skills (These are skills that candidates MUST possess)
• Design and develop process controls solutions (especially model-based control techniques) for manufacturing processes. Develop appropriate hardware and software platforms for implementing control solutions.
• Familiarity with optimization theory and controls technologies such as optimal control, robust control, adaptive control, model predictive control (MPC), non-linear approaches, and traditional PID control
• Experience with developing data-driven (system identification) / physics-based models (finite element models, mass and energy balances, etc.) for manufacturing process optimization
• Extensive knowledge of ML techniques/algorithms (e.g., neural networks, random forests, reinforcement learning, etc.) and their mathematical foundation
• Proficient in Matlab/Simulink and working knowledge of Python, C/C++, .NET.
Desired Skills
• Background in multivariate statistics tools
• Experience with real-time control systems, data acquisition, and data interpretation
• Experience using machine learning packages such as Tensorflow/Keras, PyTorch, Scikit-Learn.
• Ability to work in a manufacturing environment
Essential Non-Technical Skills
• Communication skills in a variety of situations – from gathering operator insight to stakeholder presentations
• Collaboration across a multi-disciplinary group
• Strong verbal and written skills
• Excellent interpersonal skills
• Ability to prepare and present presentations effectively
Responsibilities
• Develop physics-based models of nonlinear, multivariable systems and subsequently embed the physics of those models into algorithms to control the system dynamics by utilizing advanced model-based control design techniques
• Participate in the development of the digital platform, including AI knowledge bases (RAG) setup, frontend (VUE) and backend (Python/FastAPI) feature implementation
• Utilize machine learning methods to develop process monitoring and fault detection tools and/or classification/regression models for solving important manufacturing problems, such as process monitoring, quality analytics, and predictive maintenan
• Seeking a highly capable Control (or Data Analysis) Engineer (intern) for advancing our understanding of the advanced glass manufacturing processes. Candidates must have a strong foundation in Advanced Control Theory and a high level of expertise in physical modeling. This position requires excellent communication and interpersonal skills for working with cross-functional global teams and stakeholders.
Education and Experience (minimum required for consideration)
• Ph.D. (preferred) or Master in Chemical, Mechanical, Software, Computer Science or Electrical Engineering discipline
• Direct experience with data analysis, AI/ML, advanced controls technologies and/or first-principles/physics-based modeling
Required Skills (These are skills that candidates MUST possess)
• Design and develop process controls solutions (especially model-based control techniques) for manufacturing processes. Develop appropriate hardware and software platforms for implementing control solutions.
• Familiarity with optimization theory and controls technologies such as optimal control, robust control, adaptive control, model predictive control (MPC), non-linear approaches, and traditional PID control
• Experience with developing data-driven (system identification) / physics-based models (finite element models, mass and energy balances, etc.) for manufacturing process optimization
• Extensive knowledge of ML techniques/algorithms (e.g., neural networks, random forests, reinforcement learning, etc.) and their mathematical foundation
• Proficient in Matlab/Simulink and working knowledge of Python, C/C++, .NET.
Desired Skills
• Background in multivariate statistics tools
• Experience with real-time control systems, data acquisition, and data interpretation
• Experience using machine learning packages such as Tensorflow/Keras, PyTorch, Scikit-Learn.
• Ability to work in a manufacturing environment
Essential Non-Technical Skills
• Communication skills in a variety of situations – from gathering operator insight to stakeholder presentations
• Collaboration across a multi-disciplinary group
• Strong verbal and written skills
• Excellent interpersonal skills
• Ability to prepare and present presentations effectively
Responsibilities
• Develop physics-based models of nonlinear, multivariable systems and subsequently embed the physics of those models into algorithms to control the system dynamics by utilizing advanced model-based control design techniques
• Participate in the development of the digital platform, including AI knowledge bases (RAG) setup, frontend (VUE) and backend (Python/FastAPI) feature implementation
• Utilize machine learning methods to develop process monitoring and fault detection tools and/or classification/regression models for solving important manufacturing problems, such as process monitoring, quality analytics, and predictive maintenan
