Research Aide– LCF –Tung, Kai-Chen – 4.9.26

Argonne National Laboratory•Lemont, IL

3d•$27 - $40

About The Position

Implicit Neural Representations (INRs) have emerged as a promising approach for compactly encoding large scientific datasets while enabling continuous, differentiable access for visualization and analysis. Within the IDEAS project, INRs are being explored as a foundational data representation to support interactive, AI-assisted exploration of large-scale simulation and experimental data. This student project will investigate the use of INRs in a visualization context, focusing on understanding their behavior, tradeoffs, and suitability for interactive exploration rather than raw compression performance. The student will work with representative scientific datasets and existing INR frameworks to explore basic model construction, sampling strategies, and visualization-driven queries (e.g., evaluating fields at arbitrary spatial or temporal locations). The scope will emphasize rapid prototyping, qualitative evaluation, and exploratory analysis, with flexibility to adapt the focus based on early findings. The expected outcome is a small prototype, benchmark, or comparative study that informs Year 1 IDEAS deliverables related to efficient data representations and interactive exploration

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.