Research and Development Intern - Machine Learning - Summer 2026

Role Purpose

The intern will leverage data-driven techniques (e.g., machine learning and deep learning) to support the development of fundamental understanding of material properties for glass, glass-ceramics, and/or polymers. The intern will collect data, design test plans, and explore new ways to achieve desired material attributes. In addition, the intern will be responsible for collaborating with experimental scientists, comparing results to the literature, and writing and presenting a final report.

Key Responsibilities

• Work with scientists to collect and clean relevant data toward developing fundamental understanding aligned with project objectives
• Implement data-driven workflows to assist in the design of experiments and work with scientists to plan and carry out these experiments
• Learn new techniques and methods as needed to support the project objectives
• Document the results of the research by the end of the summer and give a final presentation of project outputs

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

Experiences/Education - Required

• Pursuing MS or PhD in Materials Science, Chemical Engineering, Mechanical Engineering, Computer Science, or similar disciplines
• Experience in applications of data-driven modeling (e.g., machine learning, deep learning, data science, statistics) to scientific domains
• Strong background in programming using Python, machine learning and deep learning packages
• Excellent verbal and written communication skills; ability to effectively present information to different audiences
• Experience working in a research lab and report writing
• Must be available for 10 weeks during Summer 2026 (May-August 2026) with a graduation date of December 2026 or later
• Be willing to work at the posted job location: Corning, NY

Experiences/Education - Desired

Knowledge/experience in:

• Materials science and structure-processing-property relationships
• Cheminformatics and representation learning
• Generative modeling and/or Large Language Models (LLMs)
• Reinforcement learning
• Other simulation methods (e.g., molecular dynamics, density functional theory, finite element analysis)
• Materials characterization techniques and interpretation