About . Imperial College London

United Kingdom

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Job description

About the role

This post (Research Assistant/Associate in Modelling Tow-Steered Composite Wing Structures) will allow you to contribute to a major industry-funded project developing the next generation of aircraft wings, led by Airbus and funded by the Aerospace Technology Institute. You will develop strong research, supervision, network, communication and project management skills. You will gain unique experience developing physically-based and AI-based failure modelling methodologies for advanced tow-steered composite laminates, working closely with Airbus and iCOMAT. You will get the opportunity to travel to international scientific conferences and to industry partners (Airbus and iCOMAT).

Your work will focus on developing failure modelling methodology for tow-steered composite laminates manufactured using automated fibre placement. Your research outcomes will underpin the development of next-generation composite wing structures with improved structural efficiency, and inform the design of future aerostructures.

What you would be doing

You will develop physically-based and AI-based failure modelling methodology for tow-steered composite laminatesy. This will involve developing and implementing physics-based failure models and AI-surrogates that account for the variable fibre orientations and stiffness distributions characteristic of steered laminates. You will work closely with iCOMAT to understand manufacturing constraints and their effect on structural performance. An important aspect will be efficient communication of your findings to academic and industry partners to inform future design of aerostructures.

What we are looking for

Research Associate: Hold a PhD in Aerospace, Mechanical Engineering or a closely related discipline, or equivalent research, industrial or commercial experience

*Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant.

Research Associate and Assistant: A recognised first class masters degree (or equivalent) in Aerospace, Mechanical Engineering or a closely related disciplineExperience in failure modelling of fibre-reinforced composites using FEM

In addition:
  • Experience in using AI for modelling of fibre-reinforced composites
  • Practical experience with coding failure models for composites
  • An interest in working in a larger group, collaborating with industry.


What we can offer you

  • The opportunity to build a strong network with contacts at Airbus and iCOMAT
  • The opportunity to develop failure models for advanced tow-steered composites that will be deployed and used in practice
  • Ample scope for travelling to international conferences to disseminate the work done.
  • Ample scope for publishing in leading journals.
  • The opportunity to gain experience with cutting-edge computational methods for novel composite manufacturing technologies.
  • The opportunity to gain supervision and project management experience.
  • The opportunity to continue your career at a world-leading institution and be part of our mission to continue science for humanity.
  • Grow your career: Gain access to Imperial's sector-leading dedicated career support for researchers as well as opportunities for promotion and progression
  • Sector-leading salary and remuneration package (including 39 days off a year and generous pension schemes).


Further information

This contract is available until 31st October 2029.

If you require any further details on the role please contact: Silvestre Pinho - [email protected]

Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities mentioned above.

If you encounter any technical issues while applying online, please don't hesitate to email us at [email protected] . We're here to help.
Job type
Full-Time
Industry
Other
Job Sector
Graduate
Job Position
Research
Estimated Salary
£45,399.00 - £59,484.00 / year
Address
United Kingdom
Post date
Closing date
Reference Number
9148714674618193580

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