Research Engineer in Machine Learning for Monitoring Mechanical Systems

Contract Type: Fixed-term for 18 months

Working Pattern: 35 hours per week

Location: Sheffield

We are seeking a machine learning researcher with a desire to work with real data from aerospace control and monitoring systems. Our research centre works closely with our industrial collaborators at Rolls-Royce to design technologies that enable the next generation of engine health monitoring enhanced by artificial intelligence.  

You will develop and apply state of the art machine learning methods to dynamic times series data from sensors and maintenance image data produced by a fleet of civil aerospace gas turbine engines.  The techniques will give faster and more focussed diagnosis of emerging real-world issues. You will contribute to exploiting these in industry systems and disseminating to high quality academic publications. 

You will be a key member of a research team with existing expertise in both machine learning and the application area. The challenge introduced by the diverse operation and disturbance conditions that aerospace engines experience will be addressed using state-of-the-art techniques including physics informed and transfer learning technologies. The software, developed in Python, will be deployed to the Rolls-Royce cloud to detect and diagnose faults in live data.

You will be able to solve the technical challenge of developing machine learning algorithms to the diverse data types and challenges of the sparse and noisy labels which are a feature of this engineering problem domain. Along with strong coding skills, you will bring a broad skill set of machine learning and signal processing for application in a cloud environment.  

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. 

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