Advanced Process Controls Intern

Role Purpose

Corning Incorporated is a world leader in specialty glass and ceramics, focusing on keystone components that enable high-technology systems for consumer electronics, mobile emissions control, telecommunications, and life sciences. Corning's Manufacturing Technology and Engineering (MT&E) division is recognized as the leader in engineering excellence and innovative manufacturing technologies.

MT&E is seeking an Advanced Process Controls Intern who is interested in applying controls, optimization, data analytics, and machine learning to real manufacturing challenges. The intern will contribute to project work in a hands-on engineering environment while learning how advanced controls technologies are developed, tested, and transferred into manufacturing. This internship offers exposure to a broad range of Corning processes and opportunities to work with experienced engineers on model-based control, numerical optimization, process monitoring, fault detection, defect classification, and predictive maintenance.

Key Responsibilities

Responsibilities will depend on the assigned project and may include:

• Support the design, development, testing, and documentation of process controls solutions for manufacturing processes while working in multidisciplinary teams.
• Analyze process data, control performance, and manufacturing trends to identify opportunities for improved stability, quality, throughput, or efficiency.
• Assist with controls-oriented modeling, system identification, simulation, and evaluation of traditional PID and model-based control strategies.
• Contribute to model predictive control, internal model control, or other advanced-control concepts under the guidance of experienced engineers.
• Build prototype optimization or analytics models using tools such as MATLAB, Simulink, Python, or related packages.
• Apply data analytics, statistical methods, or machine learning techniques to process monitoring, fault detection, defect classification, predictive maintenance, or continuous improvement problems.
• Work with process subject matter experts to understand manufacturing requirements, translate project needs into technical tasks, and communicate findings clearly.
• Prepare technical summaries, presentations, code, models, and documentation to share results with engineering and manufacturing stakeholders.
• Complete an internship project with defined deliverables and present outcomes, recommendations, and next steps at the end of the assignment.

Experiences/Education - Required

• Currently pursuing a B.S., M.S., or Ph.D. in Electrical Engineering, Mechanical Engineering, Chemical Engineering, Systems Engineering, Operations Research, Computer Science, Data Science, Applied Mathematics, or a related technical field. Coursework or project experience in process controls, optimization, data analytics, machine learning, or dynamic systems is preferred.
• Prior industry experience is not required. Relevant academic research, coursework, capstone projects, internships, co-ops, or hands-on technical projects are strongly valued.

Required Skills

• Foundational understanding of process controls, dynamic systems, statistics, optimization, or machine learning.
• Programming experience in Python, MATLAB/Simulink, or a comparable technical computing environment.
• Ability to analyze data, build models, test hypotheses, and communicate technical findings clearly.
• Strong problem-solving skills, technical curiosity, and willingness to learn new manufacturing processes and engineering tools.
• Ability to work effectively on team-based projects as well as independently assigned tasks.
• Comfort working in an engineering and manufacturing environment, including following safety expectations and site procedures.

Desired Skills

• Coursework or project experience with model predictive control, PID control, system identification, or simulation.
• Experience with numerical optimization, including LP, NLP, MILP, convex optimization, or solver-based modeling tools.
• Experience with machine learning, statistical modeling, multivariate analysis, PCA, PLS regression, or time-series analysis.
• Experience with Git, version control, data visualization, or software development practices.
• Interest in manufacturing, applied research, advanced controls, operations research, or industrial data science.
• Strong communication skills and interest in collaborating with cross-functional engineering teams.