SIP - NST - Marroquin, Marcos - 4.7.26

Argonne National Laboratory • Lemont, IL

1d • Onsite

About The Position

This internship focuses on developing machine-learning based denoising and feature extraction models for analyzing atomic-resolution electron microscopy images of ultrathin microelectronics-relevant materials. The intern will investigate methods to improve electron dose efficiency by combining data from multiple electron microscopy channels, including spectroscopy and diffraction datasets. Additionally, they will compare denoising techniques for both real-time video data and arrays of high-resolution images.

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, have graduated from an accredited institution within the past 3 months, or be actively enrolled in a graduate program at an accredited institution. Candidates must be 18 years or older at the time the appointment begins and possess a cumulative GPA of 3.0 on a 4.0 scale. Applicants must be a U.S. citizen or Legal Permanent Resident at the time of application. Pre-employment drug testing may be required based on appointment length, and all students are subject to applicable drug testing policies.

Requirements

• Currently enrolled in undergraduate or graduate studies at an accredited institution.
• Graduated from an accredited institution within the past 3 months.
• 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.

Responsibilities

• Develop machine-learning based denoising and feature extraction models to extract point defect distributions in ultrathin microelectronics-relevant materials from atomic-resolution electron microscopy images.
• Study improvements to electron dose efficiency achievable by combining multiple channels of electron microscopy data, including integrating information from correlated spectroscopy and diffraction datasets.
• Compare denoising approaches for real-time video datasets versus arrays of spatially dependent high-resolution images.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Education Level: No Education Listed
• Number of Employees: 1,001-5,000 employees