Fuel Modeling Analyst

Description

Idaho National Laboratory is hiring a Fuel Performance Modeling Analyst to work on our Fuel Development, Performance, and Qualification Department within the Nuclear Science and Technology Directorate. The successful candidate will perform advanced modeling and simulation activities focused on nuclear fuel performance, particularly supporting the integration of experimental and post-irradiation examination (PIE) data with predictive computational tools.

Our team works a 9x80 schedule located out of our EROB facility with every other Friday off.

This position will contribute to the development, application, and validation of fuel performance models using BISON, RELAP5‑3D, and similar simulation codes (SAS4A/SASSYS-1, MFUEL, FAST) to evaluate steady-state and transient behavior of nuclear fuels. The candidate will work closely with scientists and engineers across INL to provide modeling support for experiments in INL's test reactors, contributing to both experiment design and data interpretation.

The selected individual will play a key role in the integration of multi‑physics fuel modeling with thermal‑hydraulic boundary conditions, collaborating with thermal‑fluids and reactor systems experts. Over time, this position offers the opportunity to grow into a technical and/or project leadership role, guiding multi‑disciplinary teams and mentoring junior staff.

• Conduct fuel performance modeling and simulation using finite‑element methods and including tools such as BISON, RELAP5‑3D, and other similar or complementary codes.
• Support the design and analysis of fuel irradiation experiments and transient testing by providing predictive modeling and post‑test evaluation.
• Develop, calibrate, and validate material and physical models using PIE and experimental data from INL test reactors.
• Integrate thermal‑hydraulic boundary conditions with fuel performance models to ensure high‑fidelity representation of in‑reactor conditions.
• Perform sensitivity and uncertainty quantification to assess model confidence and guide future experimental efforts.
• Collaborate across INL's reactor operations, PIE, and modeling groups to advance fuel qualification efforts for various reactor types.
• Contribute to technical reports, journal articles, and conference presentations.
• Pursue new research directions and potential funding opportunities in nuclear fuel modeling and simulation.
• Demonstrate potential for leadership in project management, mentoring, and technical direction.

• Level 3:
  Master's in Nuclear Engineering, Mechanical Engineering, Materials Science or a related ABET accredited field and 2 years of relevant experience; OR a PhD in the same field and 2 years relevant experience.
• Level 4:
  Master's in the same field and 6 years of relevant experience; OR a PhD and 4 years relevant experience.
• Demonstrated experience with fuel performance codes and/or thermal‑hydraulics codes (RELAP5‑3D or similar).
• Strong background in finite element modeling, heat transfer, solid mechanics, and materials behavior under irradiation.
• Proven ability to work effectively in collaborative, multi‑disciplinary research environments.
• Excellent written and verbal communication skills with a record of technical publications or presentations.
• This position requires the ability to obtain and maintain a Department of Energy "L" clearance, which requires U.S. Citizenship.

• Experience with finite element codes and programming.
• Experience with coupled multi‑physics simulations involving fuel performance and thermal‑hydraulics (and neutronics).
• Familiarity with transient testing and/or metallic fuels.
• Working knowledge of PIE data interpretation and experiment‑informed model calibration.
• Understanding of code development practices, verification and validation (V&V), and uncertainty quantification.
• Demonstrated initiative toward leadership and mentoring roles.

• The pay range for this position is:
  Level 3 ($93,204 - $191,284) / Level 4 ($111,888 - $229,284). At Idaho National Laboratory compensation decisions are determined using factors such as education, relevant…