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.