SULI - AMD - Makar, Michael - 6.25.26

Argonne National Laboratory•Lemont, IL

6d•Onsite

About The Position

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.

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.
If accepting an offer, must pass a screening drug test.

Nice To Haves

Experience in materials science, artificial intelligence, data science, and HPC.

Responsibilities

Research material evolution during tribological failure using high-energy, high-speed X-ray diffraction (XRD) data.
Improve beamline data-processing workflows by integrating machine learning, Python-based scientific computing, and high-performance computing (HPC).
Work with a Python-based XRD analysis application that integrates GSAS-II within a parallel framework.
Optimize the XRD analysis application for scalable workflows on systems such as ALCF Crux.
Process and visualize diffraction data using heatmaps, scatterplots, strain maps, and change metrics to identify trends associated with material degradation and failure.
Explore machine learning methods to improve beamline data interpretation.
Potentially include complementary materials characterization techniques such as profilometry, microscopy, cross-sectional analysis, or electron microscopy.