Research Associate in Machine Vision Systems

We are seeking to recruit a Research Associate to develop cutting-edge sensor systems by working closely with Airbus UK to support their vision for Landing Gear of Tomorrow. This is a computer vision systems research role in our aerospace control and monitoring research centre, where you will develop algorithms that deliver novel functionality into Industry and new methods to academia.

During the 18-month post in The University of Sheffield, you will be responsible for the development of machine vision algorithms for challenging environments (vibration, fog, icing, etc.). The system will extract disturbance invariant (intrinsic) properties from landing gear test facilities housed at Sheffield and Airbus. The properties are extracted from the camera's data stream to determine the landing gear operating and health state.

You will develop novel algorithms that can work despite the arduous operating conditions and prove their effectiveness by augmenting an existing test dataset with an appropriate mix of physical experiments and synthetic image generative modelling. The planned outcomes are the deployment of the solution of full-scale industrial rigs, leading to future flight trials, and publication to leading sensor and computer vision conferences and journals.

A successful candidate will be able to take a systems approach to solve the technical challenges of working with sensor systems in extreme aerospace environments. You will make quantitative trade-offs between complexity, performance and risk in order to inform and convince industrial stakeholders of the merits of your solution. Your solution will require knowledge of fundamental methodologies for advanced vision processing, including but not limited to diffusion models and deep neural networks, along with experience of implementing machine learning and signal processing for embedded vision systems. We anticipate that this novel problem setting and broad approach will provide the foundation for you to deliver novel research into world class journals and conferences.

We build teams of people from different heritages and lifestyles from across the world, whose talent and contributions complement each other to greatest effect. We believe diversity in all its forms delivers greater impact through research, teaching and student experience.