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Research Assistant (Doctoral Researcher) in Data-Driven Modelling and Control of Fluid-Structure Interactions

Imperial College London - Department of Aeronautics

Location: London
Salary: £38,194 to £41,388 (Research Job Family, Research Assistant)
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 24th November 2022
Closes: 2nd January 2023
Job Ref: ENG02371
 

Campus: South Kensington, London

Job summary:

Applications are invited for a Research Assistant (PhD Studentship) in the Department of Aeronautics in the general area of data-driven modelling and nonlinear control of fluid structure interactions. The successful candidate will join a leading team of researchers at the heart of London, benefit from a European training network, and conduct research in collaboration with civil aviation manufacturer Airbus. The envisaged impact of this fundamental research project is to enable zero-emissions flight of future aircraft, opening opportunities for collaboration with the Department of Aeronautics’ new £25m Brahmal Vasudevan Institute for Sustainable Aviation.

The post (36 months) is funded by the UKRI as part of Imperial College’s role as Associate Partner in the Marie Skłodowska-Curie Doctoral-Training-Network (DTN) “ModConFlex”. The DTN will host 14 postgraduates across seven European academic institutions, coordinated by the University of Wuppertal, Germany. This project is supervised by Dr Andrew Wynn and Prof. Rafael Palacios and the successful applicant will join Imperial’s Aeroelastics Lab (http://www.imperial.ac.uk/aeroelastics), which specialises in computational methods for aeroelastic simulation and control of wind turbines and next-generation aircraft.

The research aims to enable nonlinear real-time control of physical systems characterized by strong fluid structure interactions. To achieve this, the project will develop a state-of-the art data-driven framework that can identify nonlinear models of minimal order directly from high fidelity aeroelastic simulations. Additionally, we will create nonlinear control algorithms that exploit the structure of the identified minimal-order models to enable real-time control in nonlinear aeroelasticity. There is potential for field testing of control algorithms with Associated Partner Airbus.

Duties and Responsibilities:

  • You will develop novel data-driven modelling approaches for nonlinear fluid-structure interactions and exploit these in real-time nonlinear control algorithms.
  • You are expected to collaborate with international project partners and attend scientific training schools from the ModConFlex DTN.

Essential Requirements: Applications must:

  • Hold (or expect to obtain) a first-class degree in Aerospace or Mechanical Engineering, Mathematics, Physics, or another related discipline.
  • Have sound knowledge of at least two of the fields of: Fluid Mechanics, Control Theory, Optimization, Data-driven Modelling, Structural Dynamics.
  • Have demonstrable proficiency in advanced computer programming.
  • Not yet hold a PhD, and expect to enrol for a PhD as part of this position.
  • Not have lived in the UK for more than 1 year in the past 3 years.

Further information:

Informal enquiries can be made to Dr Andrew Wynn: a.wynn@imperial.ac.uk

For queries relating to the recruitment process contact: Lisa Kelly: l.kelly@imperial.ac.uk

For technical issues when applying online, contact: recruitment@imperial.ac.uk

CLOSING DATE – 2nd January 2023  

To apply, visit www.imperial.ac.uk/jobs and search by the job reference ENG02371

Imperial Expectations guide the behaviour of all our staff.

Committed to equality and valuing diversity, we are also an Athena SWAN Silver Award winner, a Stonewall Diversity Champion, a Disability Confident Employer and are working in partnership with GIRES to promote respect for trans people.

The College is a proud signatory to the San-Francisco Declaration on Research Assessment (DORA), which means that in hiring and promotion decisions, we evaluate applicants on the quality of their work, not the journal impact factor where it is published. For more information, see https://www.imperial.ac.uk/research-and-innovation/about-imperial-research/research-evaluation/

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