Science & Technology - Materials Modeling Intern - Summer 2027

Corning•City of Corning, NY

3d•$42,510 - $82,810

About The Position

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 include machine-learned force field development for mixed-oxide thin films, mesoscale modeling of glass-ceramic etching, and coupled multiphysics modeling of laser damage in optical coating systems. Each project supports an active experimental or engineering program. The intern will select a project aligned with their background, develop and validate models, and present findings at the end of the summer. The role involves reviewing relevant literature, developing and running appropriate simulations, creating reproducible workflows, comparing predictions with experimental observations or higher-fidelity calculations, and communicating results and recommendations. The position is intended for someone enrolled in a relevant master's or doctoral program. The position does not support immigration sponsorship.

Requirements

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, such as numerical methods, thermodynamics, kinetics/transport, heat transfer, quantum mechanics, solid-state physics, or machine learning.
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.

Nice To Haves

Experience with atomistic simulation packages such as 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.

Responsibilities

Select a project aligned with their 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, potentially including MLFF training, molecular dynamics, density functional theory, 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 and 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.