Job Description

Machine Learning and High-Performance Computing for Synchrotron Beamline X-Ray Diffraction Analysis of Tribological Failure. High-Performance Computing & Artificial Intelligence.
Members of the Interfacial Mechanics & Materials Section will mentor the intern in research on material evolution during tribological failure using high-energy, high-speed X-ray diffraction (XRD) data. The project will focus on improving beamline data-processing workflows by integrating machine learning, Python-based scientific computing, and high-performance computing (HPC).
The intern will work with a Python-based XRD analysis application that integrates GSAS-II within a parallel framework and will help optimize it for scalable workflows on systems such as ALCF Crux. Processed results will be stored as four-dimensional Zarr datasets, enabling efficient analysis and visualization of large experimental datasets.
The intern will process and visualize diffraction data using heatmaps, scatterplots, strain maps, and change metrics to identify trends associated with material degradation and failure. The project may also explore machine learning methods to improve beamline data interpretation and may include complementary materials characterization techniques such as profilometry, microscopy, cross-sectional analysis, or electron microscopy.
This project will provide hands-on experience in materials science, artificial intelligence, data science, and HPC while contributing to scalable methods for analyzing complex experimental data.

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

Job Family

DOE Seasonal Intern

Job Profile

DOE - SULI (Science Undergraduate Laboratory Internship)

Worker Type

Contingent Worker

Time Type

Full time

Scheduled Weekly Hours

40

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.