CFD Solver Developer (LBM)

About the role

We are seeking a Computational Physicist to join our M-Star team — a small, highly effective, and efficient specialized team delivering world-class CFD software to industries including Life Sciences, Chemical Materials, and Industrial. M-Star is a modern computational fluid dynamics (CFD) platform that provides first-principles modeling tools for scientists and engineers, generating predictions functionally indistinguishable from measured data. The platform is designed to deliver high-fidelity, predictive simulations of real industrial processes such as turbulent mixing, multiphase flows, particle transport, heat and mass transfer, and chemically reactive systems. We focus on mechanistic modeling rooted in transport physics rather than empirical tuning, enabling reliable predictions across a wide range of operating conditions and scales.

Responsibilities

• Develop M-Star’s fluid dynamics solver, including maintenance, feature addition, algorithm implementation, and validation.
• Translate academic research in numerical methods into high-performance, easy-to-use software products.
• Model a wide range of physics including fluid dynamics, multiphase flows, advection–diffusion, particle mechanics, and heat transfer.
• Implement meshing algorithms and data structures for GPU architectures in a distributed memory environment.
• Work with support engineers and users to tailor software to current needs.
• Perform validation studies and present results at conferences.

Requirements

• Background in numerical methods for transport physics (fluid dynamics, advection–diffusion, particle mechanics, or heat transfer).
• Experience writing high-performance physics codes using parallel computing in shared and distributed memory systems.
• Expertise with Lattice Boltzmann Methods (LBM) for fluid simulation.
• Knowledge of the Discrete Element Method (DEM) for particle mechanics.
• Specialization in numerical methods for multiphase modeling of liquid–liquid and gas–liquid systems.
• Algorithms for computational geometry including structured/unstructured meshing, 3D search, and mesh refinement.
• Detailed understanding of GPU architectures and CUDA toolkit.
• MPI programming for multi-GPU code development.
• Experience working on a large multiphysics solver.