Computational Engineering Division Undergraduate Intern

LLNL•Livermore, CA

5d•Onsite

About The Position

Computational Engineering Division (CED) offers continuing undergraduate students the opportunity to engage in practical research experience to further their educational goals. Opportunities include technical and/or research support to projects in the areas of Electrical Engineering, Mechanical Engineering, Computational Engineering, or other related fields. This position requires full-time on-site presence due to the nature of the work.

Requirements

Must be eligible to access the Laboratory in compliance with Section 3112 of the National Defense Authorization Act (NDAA).
Must be a college or university student in good academic standing enrolled in an accredited institution pursuing an associates or bachelor’s degree, or has completed their associate's degree in engineering, physics, computer science or a related field.
A sustained record of outstanding academic performance.
Ability to work full time for six months from July 2026 - December 2026.
Ability to apply basic engineering or scientific principles to solve technical problems.
Communication skills necessary to communicate research project(s), document work and give a technical talk/presentation.
Ability to present and communicate concepts and ideas.
Effective interpersonal skills with the ability to work in a team environment.
Familiarity with one or more of the following technical computing packages common in your discipline area, such as Matlab, R, or Python.
Experience working with computers and familiarity with Microsoft Word, Excel and PowerPoint.

Responsibilities

Provide technical and/or research support for projects involving material strength characterization, additively manufactured metals, computational modeling, and AI/ML-enabled surrogate modeling.
Perform hands-on experimental work to determine material properties such as strength and stress-strain behavior using mechanical testing systems.
Support integration of stress-strain and related material data into a software workflow.
Assist in developing, training, and evaluating surrogate models using AI/ML methods for constitutive model emulation.
Prepare summaries, figures, presentations, or technical briefings to communicate research findings.
Attend relevant seminars.
Perform other duties as assigned.