Research Fellow in Machine Learning for Aeroelasticity

We are looking for a Research Fellow to join the Department of Aeronautics and Astronautics at the University of Southampton, UK. You will be working under the supervision of Prof. 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 postdoctoral research position is to support the design of elastic aircraft with a combination of aerodynamic tools of different fidelity, and the development of machine learning technologies in the context of aeroelasticity. There will also be opportunities to supervise post-graduate students and to get involved in other research streams within the same project. 

Within the Department of Aeronautics and Astronautics, you will be based in the Aerodynamics and Flight Mechanics research group 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.

The candidate should have or be close to completing a PhD in aerospace engineering (or equivalent qualification and experience), with experience in fixed/rotary wing air vehicles.

What you need:

• Strong understanding of fixed-wing and rotorcraft aerodynamics, aeroelasticity, stability & control;
• Experience with medium-fidelity solvers, e.g. UVLM, DLM, hybrid 2.5D/VLM coupling, free wake methods and vortex particle solvers; or 
• Experience with high-fidelity solvers, e.g. SU2, OpenFoam, StarCCM+, Fluent;
• Proficiency in programming, e.g. Python, Matlab, C;
• Experience utilizing high-performance computing (HPC) to parallelize workflows;
• Excellent work planning and issue resolution skills;
• Strong technical, written, and verbal communication skills.

Bonus qualifications:

• Experience with aerodynamics & aeroelastic low-/mid-/high-fidelity simulation tools for loads calculations;
• Experience with developing, training, and optimizing neural networks or other machine learning models.

For this position we are targeting a salary corresponding to Level 4 Spine Point 28 - 30. Actual compensation offered will be determined by factors such as job-related knowledge, skills, and experience. Please contact Prof Andrea Da Ronch (a.da-ronch@soton.ac.uk ) 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.