Description
You are someone who sees fluid dynamics not as an abstract academic exercise but as a set of real problems that need computational solutions that actually work. When you write Python code to model turbulence or simulate flow behavior, you are thinking about whether the results make physical sense, not just whether the script runs without errors. You have spent enough time with numerical methods to know that a simulation can be mathematically correct and still completely useless if the discretization is wrong or the boundary conditions do not reflect reality.
You do not wait for someone to hand you a perfect specification. When a research problem lands on your desk, you dig into the literature, talk to the physicists or engineers who care about the answer, and figure out what actually needs to be built. Legacy code does not scare you. You have opened Python modules or Fortran subroutines written years ago with no documentation and reverse-engineered what they were trying to do so you could extend them or fix what broke.
You care about the craft of writing code that other people can use. That means clear variable names, functions that do one thing well, and enough comments that someone six months from now can understand why you made a particular choice. Moving between Python for rapid prototyping and Fortran for performance-critical solvers feels natural to you because you understand each language has its place in the computational pipeline.
Design and implement new simulation features for fluid dynamics solvers using Python and Fortran, translating research concepts into working code that produces physically accurate results. Develop and optimize numerical solvers for computational fluid dynamics problems, balancing accuracy, stability, and performance across different flow regimes. Build unit and integration tests to validate that your implementations produce correct results against analytical solutions, experimental data, or established benchmarks. Investigate and resolve defects in existing simulation code, tracing issues through complex numerical algorithms and legacy Fortran implementations to deliver robust fixes. Write technical documentation that explains your solver design choices, numerical methods, and implementation details for other engineers and researchers who will build on your work. Prototype new computational approaches by reviewing recent literature, implementing proof-of-concept code, and evaluating whether new methods improve on existing capabilities. Collaborate with other R&D engineers and domain experts to integrate your solver components into larger multi-physics simulation workflows.
Enable engineers designing offshore wind platforms, wave energy systems, and marine structures to simulate complex fluid dynamics scenarios they could not model before, directly supporting the global transition to renewable energy. Accelerate critical design decisions for customers by delivering solver optimizations that cut simulation runtime from days to hours, keeping multi-million-dollar projects on schedule. Expand the range of problems water-based industries can solve computationally by implementing new numerical methods that handle previously intractable flow regimes or boundary conditions. Build confidence in simulation-driven design by resolving defects and delivering validation studies that prove your solvers produce results customers can trust for high-stakes engineering decisions. Reduce the barrier to entry for new users in aquaculture, coastal engineering, and marine renewable energy sectors through clear documentation and robust solver implementations that work reliably out of the box. Shape the future technical direction of fluid dynamics simulation as the team explores AI-accelerated methods and next-generation computational approaches. Strengthen the reputation of Synopsys simulation tools in water-based industries by delivering software that performs accurately under real-world conditions and scales to handle the complex problems customers actually face.