SULI– MCS – Dewasurendra, Pahan – 2.24.26

Argonne National Laboratory • Lemont, IL

6h

About The Position

Flow matching models have shown great potential in image generation tasks among probabilistic generative models. However, most flow matching models in the literature do not explicitly utilize the underlying clustering/manifold structure in the target data when learning the flow from a simple source distribution like the standard Gaussian. This leads to inefficient learning and generation, especially for many high-dimensional real-world datasets, which often reside in a low-dimensional manifold. In this study, we will review manifold-aware generative models in the literature and deploy them at scale using physical simulation data.
Education and Experience Requirements The entirety of the appointment must be conducted within the United States. Must be 18 years or older at the time the appointment begins. 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

Requirements

• The entirety of the appointment must be conducted within the United States.
• Must be 18 years or older at the time the appointment begins.
• 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

What This Job Offers

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