Role Descriptions & Responsibilities

We are looking for a CFD Turbulence Modeler candidate for the PowerFLOW core development team who enjoys inventing original solutions for computational fluid dynamics (CFD) while bridging AI/ML with traditional turbulence modeling.

You will be expected to participate in development and implementation of turbulence models and related numerical algorithms in our Lattice Boltzmann solver. You should have excellent understanding of turbulent fluid flow and heat and mass transfer physics as well as numerical algorithms, to be able to evaluate, develop, and implement turbulence models and numerical algorithms, identify and solve physics/simulator software issues. You will also support turbulence modeling activities as they pertain to automotive aerodynamics, aeroacoustics and aerothermal workflows. Your day to day responsibilities include the following:

• Design and implementation of turbulence modeling algorithms and numerical schemes

• Analysis of physics and software problems

• Tracking and fixing physics software bugs

• Manage project development progress, ensuring physics product quality,

• Working closely with other development teams for validation and application of developed scientific software.

Qualifications

• PhD degree in one of the following disciplines: Mechanical/Aerospace Engineering, Applied Mathematics, Physics, Computational Mathematics, or similar

• Educational/professional background in turbulence modeling methodology such as Reynolds-Averaged Navier-Stokes (RANS), Large Eddy Simulations, Hybrid RANS

• Knowledge of/experience with numerical analysis and CFD

• Knowledge of/experience with Machine Learning methods applications to fluid dynamics, or readiness to use ML for turbulence modeling is a strong plus

• Experience with commercial CFD software development or usage is a strong plus

• Knowledge of/experience with Large Language Models applications to physics problem solving and/or software development is a strong plus

• Excellent programming (C++) and computer skills (Linux, MPI, GDB, SVN) is a strong plus

• Demonstrated experience with scientific programming and familiarity with high performance computing (HPC) is a strong plus

• Good academic record is a plus

• Familiarity with Lattice Boltzmann methods and kinetic theory of fluids is a plus

• Demonstrated knowledge in applied aerodynamics is a plus

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