Research Aide - DSL - Siebenschuch, Carlo - 4.9.26.

Argonne National Laboratory•Lemont, IL

23h•$31 - $47

About The Position

High-Dimensional Optimization with Diffusion Models investigates the use of diffusion-based generative models as optimization mechanisms in high-dimensional search spaces. The project explores how reverse diffusion processes can be conditioned on objective functions to iteratively generate candidate solutions that concentrate around optimal regions of complex landscapes. The work will study approaches such as score-based diffusion, masked diffusion models (for discrete structures), and constraint conditioning to bias the generative process toward high-quality solutions, with a focus on scalability, multimodal objectives, and robustness in very high-dimensional settings. The goal is to develop and evaluate diffusion-driven optimization algorithms capable of efficiently exploring complex solution spaces and competing with or complementing classical optimization techniques.

Requirements

The entirety of the appointment must be conducted within the United States.
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.
If accepting an offer, candidates may be required to complete pre-employment drug testing based on appointment length. All students remain subject to applicable drug testing policies.
Must complete a satisfactory background check.

Benefits

comprehensive benefits are part of the total rewards package.
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