Visiting Student-Metcalf– MCS – Yu, Eric – 3.3.26

Argonne National Laboratory • Lemont, IL

8h

About The Position

Many modern scientific and engineering applications involve high-dimensional stochastic objectives that are both nonsmooth and derivative-free, where classical full-space trust-region methods become computationally prohibitive. This project extends deterministic nonsmooth trust-region frameworks to a scalable stochastic setting by integrating random low-dimensional subspace models, probabilistic acceptance rules, and noise-aware sampling strategies into a Python implementation. The outcome will be a modular, well-documented package implementing subspace-based stochastic trust-region algorithms, validated on noisy high-dimensional benchmarks with reproducible experiments.

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.

What This Job Offers

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