SULI - AMD - Guzman, Adrian -6.22.26
About The Position
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.
Requirements
- 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.
- Must pass a screening drug test if accepting an offer.
Nice To Haves
- Experience in experimental materials research.
- Experience in data science.
- Experience in AI-based analysis.
Responsibilities
- Develop test methods to evaluate material failure in tribological systems.
- Conduct experimental investigations using measuring microscopes and profilometry.
- Perform laboratory experiments designed to reproduce relevant service conditions.
- Analyze wear, friction, and material degradation processes.
- Integrate artificial intelligence and data-driven methods into tribological research workflows.
- Develop automated data-management pipelines for organization, storage, and curation of large experimental datasets.
- Explore the use of AI-enabled data-processing workflows and machine learning techniques to identify patterns in wear, friction, and failure data.
- Contribute to predictive modeling approaches for component degradation.
Benefits
- The entirety of the appointment must be conducted within the United States.
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What This Job Offers
Job Type
Full-time
Career Level
Intern
Education Level
No Education Listed
