We are hiring a hands on Computational Modelling Engineer to build the physics-based models and dynamic simulators behind an advanced industrial optimisation platform.
This is a practical engineering role. You will translate real physical and chemical processes into reliable Python models, validate them against operating data and deploy them into systems used for real-world decision-making.
What You'll Do
- Build mechanistic models covering transport, separation, fouling, cleaning and process recovery
- Develop dynamic simulators that predict behaviour across operating cycles
- Calibrate models using real plant and operational data
- Compare model outputs against observed results and established simulation tools
- Develop faster surrogate or hybrid models for real-time applications
- Perform parameter estimation and uncertainty analysis
- Define model assumptions, operating limits and confidence levels
- Produce simulation outputs used by optimisation and AI systems
- Investigate modelling issues and improve model accuracy
- Write tested, maintainable, production-grade Python
What You'll Bring
- Strong mathematics covering differential equations, numerical methods, linear algebra and optimisation
- Experience converting physical or chemical processes into governing equations
- At least three years of scientific Python development using tools such as NumPy and SciPy
- Experience building and calibrating process or engineering models against real data
- A practical understanding of transport phenomena and thermodynamics
- Strong software engineering habits, including unit testing and version control
- Ability to work independently while collaborating with engineers and domain specialists
- Clear, evidence-based communication
Useful Additional Experience
- Pyomo, IDAES, WaterTAP, CasADi, gPROMS or Aspen Custom Modeler
- IPOPT or other derivative-based numerical solvers
- Sparse nonlinear or mixed-integer systems
- Membrane, filtration, water or wastewater process modelling
- Fouling, flux decline, backwash or cleaning-cycle models
- Surrogate modelling, model reduction or physics-informed machine learning
- Model predictive control or reinforcement learning
- Industrial process control
- Parameter estimation and uncertainty quantification
- AI-assisted development tools such as Codex, Claude Code or Cursor
Education
A Master's degree in Mechanical Engineering, Chemical Engineering, Applied Mathematics, Physics or a related discipline is preferred. Candidates with a Bachelor's degree and substantial directly relevant modelling experience will also be considered.
