Wing Design AI and Computational Tools Researcher

SONICOM; Greater London; On-site; GBP 42,000 - 56,000; Full time

Benefits offered by this job

Sector-leading salary

39 days off

Generous pension schemes

Job summary

Imperial College London is seeking a Research Assistant or Associate to develop integrated computational tools for structural wing design, bridging aeroelasticity, structural analysis and manufacturing constraints. The role offers travel to international conferences and collaboration with Airbus in a major industry-funded project.

You will develop AI-based surrogate models, quantify uncertainties in material properties and integrate these into a multidisciplinary optimisation framework for

Qualifications

• PhD in Aerospace or Mechanical Engineering or closely related field.
• For Research Assistant, PhD not yet awarded is acceptable.
• A recognised first-class Master in Aerospace/Mechanical Engineering or related field.

Responsibilities

• Develop computational tools linking aeroelastic analysis with structural sizing and failure assessment.
• Create AI-based structural surrogate frameworks and quantify uncertainties.
• Compute failure index sensitivities using analytical or automatic differentiation.
• Integrate capabilities into a multidisciplinary optimisation framework.
• Collaborate with industry partners and travel to conferences.

Skills

FEM experience

AI surrogate models

Python

Fortran

C++

Education

PhD in Aerospace or Mechanical Engineering

First-class Master in Aerospace/Mechanical Engineering

Tools

Fortran

Python

C++

Job description

Imperial College London is seeking a Research Assistant or Associate to develop integrated computational tools for structural wing design, bridging aeroelasticity, structural analysis and manufacturing constraints. The role offers travel to international conferences and collaboration with Airbus in a major industry-funded project.

You will develop AI-based surrogate models, quantify uncertainties in material properties and integrate these into a multidisciplinary optimisation framework for