AI-Enhanced Automation Intern - Summer 2027

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, combining 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).

Using real-time data generated during testing, you will help develop intelligent decision-making algorithms to dynamically recommend additional testing under varying conditions to evaluate mechanical properties

These algorithms will leverage Bayesian optimization with Gaussian process (GP) surrogate models to adaptively select the next experimental condition, while active learning and convolutional neural networks (CNNs) will enable real-time interpretation of nanoindentation and DIC datasets. This adaptive approach ensures the testing protocol is continuously optimized to thoroughly probe material behavior. This work will be conducted in the Structural Properties Laboratory (SPL) at the Materials and Fuels Complex (MFC), Idaho National Laboratory (INL).

This team works a 4x10 schedule onsite. Start date and duration of internship vary based on project and student availability.

Required

• 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

The Ideal Candidate Will Possess

• 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

Compensation and Benefits

PhD Internships Pre-Candidacy ($36.83/hour - $39.33/hour)

PhD Internships Post-Candidacy ($38.65/hour - $41.29/hour)

Eligible interns are provided with paid holiday time off, and many of our internships offer travel reimbursements.

Multi-Level: Internships are typically multi-level posting and you will be placed at the appropriate level dependent on degree field and level of education.

Physical requirement

While performing the duties of this classification, the employee is frequently required to stand, walk, sit, stoop, kneel, bend, use hands to handle materials, manipulate tools, type on a keyboard, reach with hands and arms, and operate job-related equipment. The employee must occasionally lift and/or move up to 35 pounds without assistance. Sufficient visual acuity and hearing capacity to perform the essential functions and interact with people is required. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions.