Research Aide– MCS – Omar, Nuzaer – 4.10.26

Argonne National Laboratory • Lemont, IL

2d • $31 - $47

About The Position

The student will work on generating and curating multimodal datasets from heterogeneous sources such as sensor streams, imagery, and contextual metadata to support development of the domain-aware AI analyst. They will help preprocess the data, construct aligned representations across modalities, and generate embeddings that can be used for downstream reasoning and anomaly analysis. Their work will focus on building high-quality benchmark datasets and evaluating data fidelity, consistency, and completeness. This effort will provide the structured training and evaluation data needed for the project’s multimodal learning pipeline. The student will next use existing large multimodal models and related foundation-model capabilities to train and adapt models on the project’s multimodal monitoring data. They will evaluate model behavior on tasks such as anomaly detection, contextual interpretation, and cross-modal reasoning, and analyze how training choices affect performance. A major part of their work will be benchmarking model accuracy and robustness as a function of data quality, representation choice, and modality combinations. This will help identify which modeling approaches are most effective for mission-relevant monitoring scenarios. Education and Experience Requirements The entirety of the appointment must be conducted within the United States. Applicants must be: o Currently enrolled in undergraduate or graduate studies at an accredited institution. o Graduated from an accredited institution within the past 3 months; or o 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.

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
• 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

• generating and curating multimodal datasets from heterogeneous sources such as sensor streams, imagery, and contextual metadata
• help preprocess the data
• construct aligned representations across modalities
• generate embeddings that can be used for downstream reasoning and anomaly analysis
• building high-quality benchmark datasets and evaluating data fidelity, consistency, and completeness
• use existing large multimodal models and related foundation-model capabilities to train and adapt models on the project’s multimodal monitoring data
• evaluate model behavior on tasks such as anomaly detection, contextual interpretation, and cross-modal reasoning
• benchmarking model accuracy and robustness as a function of data quality, representation choice, and modality combinations

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Education Level: No Education Listed
• Number of Employees: 1,001-5,000 employees