SULI - MCS - Agyemang, Dylan - 2.24.26.

Argonne National Laboratory • Lemont, IL

8h

About The Position

This project aims to develop flow-based generative models grounded in a potential mean-field game formalism to construct a probability path connecting the source and target distributions. With additional customized cost terms, this path is expected to reach the data manifold more faithfully, leading to high-quality samples that satisfy manifold characteristics, such as zero PDE residuals for solution fields. Moreover, we aim to recover the Boltzmann energy of the target distribution through controlled transport. The resulting energy-based model will then be applied to anomaly detection, model composition, and inverse problems.

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Education Level: No Education Listed