Job Description

Requisition Number: 79307

The company built on breakthroughs. ​ 

Join us.​ 

Corning is one of the world’s leading innovators in glass, ceramic, and materials science. From the depths of the ocean to the farthest reaches of space, our technologies push the boundaries of what’s possible.  ​ 

 How do we do this? With our people. They break through limitations and expectations – not once in a career, but every day. They help move our company, and the world, forward. ​ 

​ At Corning, there are endless possibilities for making an impact. You can help connect the unconnected, drive the future of automobiles, transform at-home entertainment, and ensure the delivery of lifesaving medicines. And so much more.​  

​ Come break through with us. 

Role Purpose

Corning's research team is hiring a graduate-level intern for Summer 2027 to contribute to computational materials modeling at the intersection of simulation and experimental science. Projects span machine-learned force field (MLFF) development for mixed-oxide thin films, mesoscale modeling of glass-ceramic etching, and coupled multiphysics modeling of laser damage in optical coating systems — each tied to an active experimental or engineering program. You'll select a project aligned with your background, develop and validate models, and deliver findings at the close of the summer.

Program Perks:

• Competitive salaries
• Assistance with housing and travel
• Meaningful project and final project presentation

Intern Activities:

• Social and networking opportunities
• Weekly tech talks
• Community involvement

Key Responsibilities

• Select a project aligned with your background; review relevant literature and develop a focused modeling plan with clear validation criteria
• Set up, run, and analyze simulations using methods appropriate to the selected project -- which may include MLFF training, molecular dynamics (MD), density functional theory (DFT), mesoscale modeling, or COMSOL Multiphysics
• Build reproducible scripts and workflows for model construction, parameter studies, data analysis, and visualization
• Compare model predictions against experimental observations or higher-fidelity calculations; quantify uncertainty and sensitivity to key parameters
• Deliver a documented model or workflow, a concise technical summary, and a final presentation with results and recommendations for follow-on work

Experiences/Education - Required

• Currently enrolled in an accredited Master's or doctoral degree program in Materials Science, Chemistry, Chemical Engineering, Physics, Mechanical Engineering, Computational Science, or a related discipline
• Completed foundational coursework in materials science and at least one relevant area (e.g., numerical methods, thermodynamics, kinetics/transport, heat transfer, quantum mechanics, solid-state physics, or machine learning)

Required Skills

• Working proficiency in Python or another scientific programming language, including data analysis and visualization
• Familiarity with generative AI tools and agentic workflow frameworks
• Experience working in a Linux and/or high-performance computing (HPC) environment
• Prior research or project experience using computational methods

Desired Skills

• Experience with atomistic simulation packages (e.g., LAMMPS, VASP, or ASE -- Atomic Simulation Environment)
• Experience training, fine-tuning, or validating machine-learning models for materials or molecular systems
• Experience with COMSOL Multiphysics, finite-element methods, or coupled multiphysics simulation
• Familiarity with thin films, optical coatings, glass or glass-ceramics, etching, defects, or laser-material interactions
• Experience with version control, workflow automation, statistical analysis, or uncertainty quantification

Soft Skills

• Scientific curiosity and willingness to learn unfamiliar modeling methods and materials systems.
• Structured problem solving, attention to detail, and sound judgment when interpreting incomplete or uncertain data.
• Clear written and verbal communication with both computational and experimental collaborators.
• Ability to work independently, seek input early, manage priorities, and respond constructively to technical feedback.
• Collaborative mindset and commitment to reproducible, well-documented work

This position does not support immigration sponsorship.

​The range for this position is $1,635.00 - $3,185.00 biweekly. Starting pay for the successful applicant is prescribed based on geographic location, nature of position, and year in school.

A job that shapes a life. 

We prohibit discrimination on the basis of race, color, gender, age, religion, national origin, sexual orientation, gender identity or expression, disability, veteran status or any other legally protected status.

Corning is committed to providing equal employment opportunities and considers requests for reasonable accommodations in accordance with applicable laws. Individuals with disabilities or sincerely held religious beliefs may request reasonable accommodations to participate in the application or interview process, perform essential job functions, or access other benefits and privileges of employment. To submit a request for reasonable accommodation related to disability or religion, please contact us at accommodations@corning.com.