Help develop the Python tools behind early aircraft design decisions. You will build on ERC’s existing multi-objective design optimisation framework, extending its functionality and improving the engineering models used in initial aircraft sizing.

A central part of your work is capturing the physical effects and design dependencies that matter early in development, and translating findings from higher-fidelity analyses, supplier data and testing into efficient models for sizing loops and optimisation studies. This helps ERC make better-informed design decisions early on.

• Develop Python functionality for aircraft sizing, design exploration and multi-objective optimisation

• Add and improve models for performance, aerodynamics, propulsion, energy and mass with the relevant engineers

• Capture the physical relationships, constraints and interactions that drive early design decisions at sizing-level detail

• Build and validate surrogate models based on higher-fidelity simulation results and engineering data

• Support trade studies by comparing aircraft concepts, investigating sensitivities and weighing competing design objectives

• Strengthen modularity, interfaces, documentation and testing so the framework stays reliable and easy to extend

• Create plots and summaries that help engineers interpret results, recognise model limitations and identify infeasible designs

• Current studies in aerospace or mechanical engineering, computer science, applied mathematics or a related discipline

• Practical Python experience and an interest in scientific computing

• An interest in aircraft design and the physics behind computational models

• Familiarity with numerical methods, data analysis or optimisation

• The habit of questioning assumptions, checking units and physical plausibility, and documenting model limitations

• A hands-on, self-reliant approach and the initiative to investigate problems and see them through

• Enjoyment of collaborating with engineers, sharing progress and seeking input when needed
  
  
  Moreover, it would be helpful if you also
  
  

• have worked with NumPy, SciPy, pandas or similar libraries

• have experience with Git and automated testing

• bring some exposure to surrogate modelling or multi-objective optimisation