AI-Enhanced Automation Intern - Summer 2027
About The Position
The AI-Enhanced Automation Intern will support development of an integrated, autonomous platform for testing mechanical properties. The platform combines nanoindentation and tensile testing with digital image correlation (DIC) and electron backscattered diffraction (EBSD) characterization in an automated workflow. Using real-time testing data, the intern will help develop intelligent algorithms that recommend additional tests under varying conditions to evaluate mechanical properties. The algorithms will use Bayesian optimization with Gaussian process (GP) surrogate models to select subsequent experimental conditions adaptively. Active learning and convolutional neural networks (CNNs) will support real-time interpretation of nanoindentation and DIC datasets, helping continuously optimize test protocols to probe material behavior. The work takes place in the Structural Properties Laboratory (SPL) at the Materials and Fuels Complex (MFC), Idaho National Laboratory (INL). The team works onsite on a 4x10 schedule. The internship start date and duration vary according to project and student availability.
Requirements
- Be enrolled full time in a PhD program at an accredited college or university studying Computer Science, Artificial Intelligence, Machine Learning, or Data Science.
- Have a minimum overall GPA of 3.0.
- Be authorized to work in the U.S., including through CPT or OPT.
- Provide a current resume or CV.
- Provide unofficial transcripts that include current and completed degree programs.
- Provide a current class schedule and number of credits.
Nice To Haves
- Experience with Bayesian optimization and Gaussian process surrogate models for adaptive experimental design.
- Experience developing or applying CNNs for image-based data interpretation.
- Familiarity with active learning methods for real-time or iterative decision-making.
- Exposure to materials characterization techniques such as nanoindentation, DIC, or EBSD, or to automated/autonomous experimental platforms, through coursework, research, or a prior internship.
Responsibilities
- Support development of an integrated and autonomous platform for testing mechanical properties.
- Help combine mechanical testing modes, including nanoindentation and tensile tests, with DIC and EBSD characterization in an automated workflow.
- Help develop intelligent decision-making algorithms that recommend additional tests using real-time data.
- Use or develop Bayesian optimization with Gaussian process surrogate models to adaptively select experimental conditions.
- Apply active learning and CNNs to interpret nanoindentation and DIC datasets in real time.
Benefits
- Paid holiday time off.
- Travel reimbursement may be available for eligible interns.
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What This Job Offers
Job Type
Full-time
Career Level
Intern
Education Level
Ph.D. or professional degree
