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TOD Controls Engineer

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Date: Nov 14, 2022

Location: Wuhan, HB, CN, 430040

Company: Corning

Day to Day Responsibilities

  • Establish goals and objectives in conjunction with Group Leader and manage the engineering activities in electrical and control system to achieve the said objectives.
  • Support operations personnel via trouble shooting and provide training as needed.
  • Provide project leadership for facility processes - including feasibility studies, planning, AR sponsorship, complete documentation, i.e., generally accepted project management guidelines.
  • Design, modify, and recommend equipment types to meet or exceed customer requirements in operation control.
  • Lead developments and propose engineering programs in electrical and control engineering that can improve or enhance the current process capabilities.
  • Major areas are in (but not restrict to) cut room and 2nd packing (link to inner packing) facilities.

 

Education & Experiences

  • Ph.D. or Masters (+3 years of manufacturing experience) in Chemical, Mechanical or Electrical Engineering discipline.
  • Direct experience with data analysis, machine learning (ML), advanced controls technologies and/or first-principles/physics-based modeling.

 

 

Required Skills

  • Evaluate existing process control strategies and propose and develop enhancements. Utilize multivariate statistical methods and machine learning for process monitoring, fault diagnosis and isolation, defect classification, and/or predictive maintenance;
  • Design and develop process controls solutions (especially model-based control techniques) for manufacturing processes. Work closely with manufacturing to 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, etc.) and their mathematical foundation;
  • Proficient in Matlab/Simulink and working knowledge of Python, C/C++, .NET;
  • Must have:

        (1) a thorough understanding of relevant scientific concepts, principles, and theory, particularly relating to general machine learning and algorithms and fundamental physics principles;

        (2) experience demonstrating broad application of those concepts in real-world settings.