SULI - AMD - Guzman, Adrian -6.22.26

Development of AI-Enabled Tribological Testing and Failure Prediction Methods: Artificial Intelligence.
The intern will work with a team of research scientists and engineers on projects aimed at understanding and preventing failure in drivetrain and other tribological components. These efforts will involve the development of test methods to evaluate material failure in tribological systems, experimental investigations using measuring microscopes and profilometry, and laboratory experiments designed to reproduce relevant service conditions. The resulting measurements will provide the experimental foundation for analyzing wear, friction, and material degradation processes.
A major emphasis of the project will be the integration of artificial intelligence and data-driven methods into tribological research workflows. The intern will contribute to the development of automated data-management pipelines for the organization, storage, and curation of large experimental datasets generated during tribological testing. In parallel, the project will explore the use of AI-enabled data-processing workflows and machine learning techniques to assist in identifying patterns in wear, friction, and failure data, improving the efficiency, scalability, and consistency of analysis.
By combining experimental tribology with artificial intelligence, the project seeks to advance from conventional post-test interpretation toward predictive modeling approaches capable of identifying early indicators of component degradation prior to catastrophic failure. This work will provide the intern with hands-on experience in experimental materials research, data science, and AI-based analysis while contributing to improved methods for evaluating and optimizing the durability and reliability of engineering components.

Education and Experience Requirements

• The entirety of the appointment must be conducted within the United States.
• Applicants must be:

‒ Currently enrolled in undergraduate or graduate studies at an accredited institution.

‒ Graduated from an accredited institution within the past 3 months; or

‒ Actively enrolled in a graduate program at an accredited institution.

• Must be 18 years or older at the time the appointment begins.
• Must possess a cumulative GPA of 3.0 on a 4.0 scale.
• Must be a U.S. citizen or Legal Permanent Resident at the time of application.
• If accepting an offer, must pass a screening drug test

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EEO Information

As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.

Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.