CFD Solver Developer | M-Star

At Dotmatics, we believe science, data, and decision‑making must be deeply intertwined for innovation to thrive. Our Portfolio includes Luma, Luma Lab Connect, ELN Platform, Graphpad Prism, Geneious, Snap Gene, Protein Metrics, OMIQ, FCS Express, Lab Archives, NQuery, Easy Panel, MStar, Soft Genetics and Virscidian.

We have a vision for a new Lab of the Future that will change the future of scientific research.

We have created the world’s most comprehensive digital science platform – best‑of‑breed software applications already used by more than 2 million scientists, together in a single ecosystem united by a powerful, flexible enterprise data platform. This is not flat data buried away in digital graveyards. This is dynamic, multi‑dimensional decision‑making.

Scientific enterprises need a new level of effectiveness to achieve tomorrow’s breakthroughs. Illness will not wait. The biosphere will not wait. We are tireless in our vision, because the time for innovation is now.

Shaping the Future of Science At Dotmatics

Our global team of more than 800 colleagues are dedicated to supporting our customers in over 180 countries. Together, with our scientific community of users, we accelerate scientific innovation in order to make the world a healthier, cleaner, and safer place to live.

You’ll join a collaborative, global team pushing the boundaries of scientific innovation. Your ideas and efforts will have a tangible impact, accelerating scientific progress and discovery. We offer a dynamic, remote‑friendly environment that fosters high integrity and collaboration, empowering you to excel. Dotmatics is a company built by scientists, for scientists. Combined, we are now the world’s largest cloud‑based scientific research R&D platform.

We need your help to keep growing and pioneering the future.

We are Science Driven. We are Customer Centric. We are Better Together.

We are seeking a CFD Solver Developer, an expert in Computational Fluid Dynamics, to help develop, extend, and validate our core solver technology. This role is ideal for someone who enjoys working at the intersection of numerical methods, high‑performance computing, and real‑world engineering applications. The successful candidate will 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.

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 modelling rooted in transport physics, rather than empirical tuning, enabling reliable predictions across a wide range of operating conditions and scales.

In this role you will get to

• Develop M-Star’s fluid dynamics solver including maintenance, feature addition, algorithm implementation, and validation
• Translate academic research in numerical methods into a high‑performance, easy‑to‑use software product
• 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

We are looking for people with a background in numerical methods for transport physics (fluid dynamics, advection‑diffusion, particle mechanics, or heat transfer) ideally with a PhD in chemical engineering, mechanical engineering, or physics. You will demonstrate expert experience in writing high performance physics codes using parallel computing in shared and distributed memory systems

The Key skills we are looking for

• Working with lattice Boltzmann methods (LBM) for fluid simulation
• Knowledge of the discrete element method (DEM) for particle mechanics
• Specialization in numerical methods for multiphase modelling 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
• Familiarity with debugging and profiling tools for CUDA/MPI applications