Research Aide - CPS - Lee, Huiju - 3.25.26.

Argonne National Laboratory•Lemont, IL

4d•$31 - $47

About The Position

This project focuses on developing and benchmarking multi-modal AI agents capable of performing expert-level visual inspections of molecular and crystalline structures. Researchers in chemistry and materials science frequently use visual intuition to identify reactive sites, characterize structural features, and verify complex assemblies. We aim to automate these tasks using state-of-the-art artificial intelligence. The intern will perform a comparative analysis between proprietary frontier models, accessed through Argo and SageAPI, and open-weight alternatives hosted on the ALCF Inference Server. The primary objective is to evaluate how these agents interpret 2D and 3D renderings to identify key chemical features, recognize structural anomalies, and assess the geometric integrity of complex atomic systems. By establishing a performance baseline across these diverse inference platforms, the project will help determine the feasibility of deploying digital lab assistants to accelerate high-throughput scientific workflows.

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.

Responsibilities

Perform a comparative analysis between proprietary frontier models, accessed through Argo and SageAPI, and open-weight alternatives hosted on the ALCF Inference Server.
Evaluate how these agents interpret 2D and 3D renderings to identify key chemical features, recognize structural anomalies, and assess the geometric integrity of complex atomic systems.
Establishing a performance baseline across these diverse inference platforms

Benefits

comprehensive benefits are part of the total rewards package.
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