Nuclear Digital Thread Intern: Reactor Modeling & AI Tools

Idaho National Laboratory; Idaho Falls (ID); On-site; USD 37,000 - 57,000; Full time

Benefits offered by this job

Paid holiday time off

Travel reimbursement

Job summary

Idaho National Laboratory in Idaho Falls is seeking an Undergraduate to PhD student intern to support nuclear reactor modeling and the development of automated data pipelines that link neutronics, thermal-hydraulic, structural, and multiphysics simulations. Work includes Python tools, data management, and model integration to improve traceability and efficiency across the design lifecycle.

You will work onsite on a 9x80 schedule, with potential course credit.

Qualifications

• Undergraduate Junior/Senior, Master’s or PhD student enrolled full time in Nuclear/Mechanical Engineering, Computer Science or related field
• Minimum overall 3.0 GPA
• Authorization to work in the U.S. (CPT/OPT)
• Experience with Python
• Experience with reactor analysis tools (OpenMC, RELAP, MCNP, SERPENT, Griffin, MOOSE)
• Experience with FEA or CFD
• Current resume or CV
• Unofficial transcripts (current and completed degrees)
• Current class schedule and credits

Responsibilities

• Develop Python-based tools for automation, data management, visualization and model integration
• Support reactor simulations and data pipelines
• Explore AI-assisted coding to enhance productivity and analysis workflows
• Contribute to integrated analysis frameworks for next-generation reactor design and decision making

Skills

Python

AI-assisted coding tools

Reactor analysis tools

Finite element analysis

CFD

Education

Undergraduate/Graduate student in Nuclear/Mechanical Engineering or CS

Tools

OpenMC

RELAP

MCNP

SERPENT

Griffin

MOOSE framework

FEA

Job description

Idaho National Laboratory in Idaho Falls is seeking an Undergraduate to PhD student intern to support nuclear reactor modeling and the development of automated data pipelines that link neutronics, thermal-hydraulic, structural, and multiphysics simulations. Work includes Python tools, data management, and model integration to improve traceability and efficiency across the design lifecycle.

You will work onsite on a 9x80 schedule, with potential course credit.