The Nuclear Science and Engineering (NSE) Division is seeking a postdoctoral appointee to develop computational methods and computer codes to model the physics and engineering of advanced nuclear reactors; analyze the multi-physics aspects of nuclear fission/fusion reactors including neutronics, thermal-hydraulics, structural mechanics, and fuel behavior. Activities may involve reactor analysis including transient analysis, engineered system evaluation, and machine learning applications in modeling thermal fluid behavior of interest to reactor analysis. The candidate is expected to produce papers and reports describing results of development work, analyses, and investigations.

Position Requirements

• Knowledge of nuclear engineering principles, theories, and practices for the design and analysis of reactor systems.

• Knowledge of heat transfer and fluid dynamics for nuclear system applications and reactor safety analyses.

• Knowledge of computation methods and numerical solvers for engineering applications.

• Knowledge of modern software development practices and programming techniques.

• Skills in the use of computers and in the analysis of computational results, in the clear and effective documentation and communication of results, and in interaction with co-workers and support personnel is required.

• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.

• Ability to make our laboratory a safe, welcoming, inclusive, and accessible environment where all can thrive.

Minimum Education and Experience Requirements: This level of knowledge is typically achieved through a formal education in Nuclear Engineering, Physics, or a related field at the PhD level with zero to five years of employment experience.

Preferred Knowledge, Skills, and Experience

• Knowledge of reactor transient and accident analysis methods and experience in using system codes (e.g. RELAP5, TRACE, SAM, or SAS4A/SASSYS-1).

• Knowledge of the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework.

• Experience in computational modeling of advanced reactor designs including both fission and fusion systems.

• Knowledge of machine learning algorithms and experience in machine learning applications related to thermal fluid problems and/or nuclear reactor analysis.

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