Science & Technology – Materials Modeling Intern

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