Cfd solver developer
Pubblicato il 15-08-2026 - Siemens Digital Industries Software in Italia
We are Siemens Siemens Digital Industries Software is a leading provider of solutions for the design, simulation, and manufacture of products across many different industries. Formula 1 cars, skyscrapers, ships, space exploration vehicles, and many of the objects we see in our daily lives are being conceived and manufactured using our software. Are you a Physicist/Engineer who enjoys working at the intersection of numerical methods, high-performance computing, and real-world engineering applications? We're hiring a CFD Solver Developer to join our M-Star team – a small, yet highly effective and efficient specialized team, who deliver world class CFD software to multiple industries including Life Sciences, Chemical Material, and Industrial. M-Star is modern computational fluid dynamics (CFD) software that provides first-principles modeling tools for scientists and engineers, generating predictions that are functionally indistinguishable from measured data (). M-Star is building GPU-native, multiphysics solver for complex fluid and particle systems.
Our platform is designed to deliver high-fidelity, predictive simulations of real industrial processes, including 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. If you are seeking a role where you can contribute directly to solver architecture, algorithm development, GPU acceleration, and multiphysics model implementation, while collaborating with users and support engineers to ensure the software remains both scientifically rigorous and practically useful then we'd love to hear from you You'll make a difference by: Developing M-Star's fluid dynamics solver including maintenance, feature addition, algorithm implementation, and validation. Translating academic research in numerical me