Research Fellow in Multi-Disciplinary Analysis and Optimisation

Southampton, United Kingdom
£33,314 to £35,333 Per annum
02 Feb 2023
End of advertisement period
14 Mar 2023
Contract Type
Fixed Term
Full Time

Aeronautical and Astronautical Engineering

Location:  Boldrewood Campus
Salary:   £33,314 to £35,333 Per annum
Full Time Fixed Term (31/12/2025)
Closing Date:  Tuesday 14 March 2023
Interview Date:   To be confirmed
Reference:  2081822DA-R

You will join the Aerodynamics and Flight Mechanics (AFM) Research Group at the University of Southampton. You will be working under the supervision of Dr Andrea Da Ronch on the project “Out of Cycle Next Generation Highly Efficient Air Transport (ONEheart)”. The project is a large, national effort led by Airbus Operations Ltd with several industrial and academic partners. The aim of this position is to perform multi-disciplinary analysis and optimisation of derived and novel aircraft configurations using a monolithic architecture by taking full advantage of fast aerodynamic tools (medium fidelity and data-driven approaches) coupled with structural dynamics tools. There will also be opportunities to supervise post-graduate students and to get involved in other projects in the fields of data-driven aerodynamic models and aircraft design development.

The AFM research group is comprised of experts in theoretical, computational and experimental methods and our aim is to provide an environment in which these different approaches can be combined and focused on particular topics of practical importance. You will join a vibrant team of other postdoctoral researchers and graduate students working in different areas of aerospace and aeronautics, covering the entire spectrum of fidelity levels.

You will have or be close to completing a PhD in multi-disciplinary optimisation of fixed/rotary wing aircraft. Experience with developing models and methods for fixed/rotary wing aircraft optimisation is critical. Experience with SU2, or other aerodynamic analysis tools (medium to high fidelity preferred), and machine learning (pytorch, tensorflow), being comfortable with analysis tools such as MATLAB, Python, and high-performance computing are highly desirable. Strong communication and language skills, as evidenced by publications and presentations, are essential. 

Please contact Dr Andrea Da Ronch ( for further details about the position. 

Applications will be considered from candidates who are working towards or nearing completion of a relevant PhD qualification.  The title of Research Fellow will be applied upon completion of PhD.   Prior to the qualification being awarded the title of Senior Research Assistant will be given.’

Application Procedure

You should submit your completed online application form at The application deadline will be midnight on the closing date stated above. If you need any assistance, please call Sian Gale (Recruitment Team) on +44 (0) 23 8059 2750, or email Please quote reference 2081822DA-R on all correspondence.

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