AI-Enhanced Automation Intern

About the Role

Reporting to a Senior Manager within the Post-Irradiation Examination Characterization Department, you will support the development of an integrated and autonomous platform for testing mechanical properties. This platform combines multiple testing modes and length scales into a single automated workflow. The platform integrates mechanical testing through nanoindentation and tensile tests, alongside characterization using digital image correlation (DIC) and electron backscattered diffraction (EBSD).

Key Responsibilities

• Support the development of an integrated and autonomous platform for testing mechanical properties.
• Develop intelligent decision-making algorithms using real-time data generated during testing to dynamically recommend additional testing under varying conditions to evaluate mechanical properties.
• Leverage Bayesian optimization with Gaussian process (GP) surrogate models to adaptively select the next experimental condition.
• Enable real-time interpretation of nanoindentation and DIC datasets using active learning and convolutional neural networks (CNNs).
• Ensure the testing protocol is continuously optimized to thoroughly probe material behavior.

Requirements & Tech Stack

• Enrolled full time in a PhD program at an accredited college/university studying Computer Science, Artificial Intelligence, Machine Learning, or Data Science.
• A minimum overall 3.0 GPA.
• Authorization to work in the U.S. (including CPT and OPT).
• Current Resume or CV.
• Unofficial Transcripts (include current and completed degree programs).
• Current class schedule and number of credits.

Qualifications

• Experience with Bayesian optimization and Gaussian process surrogate models for adaptive experimental design.
• Experience developing or applying convolutional neural networks (CNNs) for image-based data interpretation.
• Familiarity with active learning methods for real-time or iterative decision-making.
• Exposure to materials characterization techniques (e.g., nanoindentation, DIC, EBSD) or automated/autonomous experimental platforms, through coursework, research, or prior internship.

Location: Structural Properties Laboratory (SPL) at the Materials and Fuels Complex (MFC), Idaho National Laboratory (INL)

Schedule: 4x10 onsite

Internship duration varies based on project and student availability.