PhysicsX - Principal CFD Engineer - Multiphase

• Proficiency in CFD solvers across open-source OpenFOAM and commercial platforms as a plus (Star-CCM+, Fluent or equivalent), including custom solver and boundary condition development. • Develop multiphase flow models for gas-liquid, solid-liquid, VOF, Euler-Euler/Euler-Lagrange approaches) across a range of industrial and process engineering applications. • Model non-Newtonian and complex fluid rheology modelling, including high-viscosity, shear-thinning, or particle-laden flow regimes • Reactive and coupled-physics flow modelling — link CFD with reaction kinetics, heat transfer, or domain-specific process models (e.g. biokinetic, combustion, or electrochemical frameworks) • Own meshing generation and strategy for complex industrial geometries, including rotating machinery, internal flow passages, spargers, and free-surface interfaces • Build robust parametric CAD models (NX, CATIA, or equivalent) tightly coupled with simulation pipelines, enabling automated design optimisation and DoE studies • Multi-physics model development end-to-end: geometry clean-up, meshing, solver setup, post-processing, and experimental data integration for model validation • Scale-up/scale-down methodology — translate small-scale experimental data to full-scale CFD models and iterating model fidelity against physical measurements • HPC experience: job scheduling, MPI-based distributed computing, GPU acceleration, and performance tuning for large-mesh transient simulations on cloud (Flux) and on-premise resources • Surrogate modelling and ML-CFD coupling — build reduced-order or AI surrogate models from high-fidelity CFD data to support design space exploration and process optimisation • Data pipeline literacy — structuring and curate CFD output datasets for downstream AI/ML training, active learning, and Pareto optimisation workflows; applying data sampling techniques (LHS, quasi-random, adaptive) to efficiently cover design space • Experimental validation workflows — compare simulation predictions against physical test data, interpreting discrepancies, and systematically improving model fidelity • Customer-facing delivery — partner with clients to scope and address complex engineering challenges via CAE and AI solutions; communicating results clearly, recommending actionable next steps, and balancing accuracy with efficiency under commercial deadlines • Engineering and workflow • 5–7 years post-graduate experience • MEng, MSc, or PhD in mechanical, chemical, or process engineering • Python scripting and simulation automation • DoE, surrogate modelling, and design space exploration • Parametric CAD modelling (NX or CATIA advantageous) • Strong written and verbal communication with technical and non-technical audiences