Overview
We are seeking a Research Associate to develop and verify a high-reliability detection system for mechanical bearing failures in next-generation aviation electrical generators. These components are vital for "more-electric" gas turbine engines but require uncompromising safety and reliability for flight. In this critical application, there is no room for missed detections or false alarms. You will devise detection systems robust to extreme environments while identifying symptoms unique to imminent mechanical failure.
New knowledge is needed for the complex, late-stage failure mechanisms of these coupled bearing systems. Using your mechanical engineering expertise, you will develop a combination of physical experimental designs and mathematical behavioural models to validate failure hypotheses. Working with our industrial and academic research team, this data will inform your design of signal-processing algorithms sensitive specifically to failure behaviours.
At the conclusion of this 18-month post, you will demonstrate your algorithms and validation approach on sub-scale rigs you have jointly designed and built. Successful technologies will then be transferred to Rolls‑Royce for a demonstrator engine and ultimately integrated into the design of the UltraFan engine.
You will be a mechanical engineer with a "systems mindset" who can complement our team’s existing expertise in electrical and control engineering. While you will lead mechanical modelling and experimental design, a desire to master signal processing and machine learning is essential to bridge the gap between disciplines and deliver an optimised, integrated solution.
Main Duties and Responsibilities
* Plan and execute research in accordance with the project aims and in collaboration with staff from the University and Rolls‑Royce.
* Design, implement and test mechanical failure detection algorithms for electrical machines.
* Build a modular bearing system model at an appropriate fidelity to represent nominal behaviour, disturbances and failure.
* Design and run experimental plans to generate data.
* Management and preprocessing of data to calibrate models and validate detection algorithms.
* Collaborate with the technical workshop team to build a bearing failure rig with disturbance injection capability.
* Collaborate with colleagues in other disciplines to understand the system interactions and fuse available signals that might contribute to reliable detection of mechanical failure.
* Expand detection and validation to incorporate other failure modes as identified by the project team.
* Perform background research to understand the current state of the art and trial appropriate techniques from the scientific literature.
* Lead the authorship of journal and conference papers.
* Write progress reports for industrial collaborators and meet any other reporting obligations.
* Liaise closely with the UTC, University and Rolls‑Royce staff both on and off-site, travelling as necessary to support project activities.
* Provide verbal presentation of results along with supporting material.
* Maintain accurate and complete records of all findings.
* Maintain awareness of current related research within the field.
* Provide technical guidance and assistance to other members of staff and students.
* As a member of staff you will be encouraged to make ethical decisions in your role, embedding the University sustainability strategy into your working activities wherever possible.
* Carry out other duties, commensurate with the grade and remit of the post.
Criteria
* An undergraduate degree (or equivalent experience) in Engineering or related discipline.
* Hold a relevant PhD degree (or close to completion) or have equivalent experience in engineering.
* In‑depth knowledge of the failure modes and effects for bearing system failures.
* Experience in low order modelling of rotating thermo‑mechanical systems and their wider system interactions.
* Design and execution of practical experiments involving data acquisition systems and hardware‑in‑the‑loop technologies.
* Experience in understanding and implementing ideas from relevant academic research papers.
* Effective communication skills, both written and verbal, with report‑writing experience.
* Ability to work cooperatively and proactively with internal colleagues and external industrial partners.
* Experience in implementing signal processing and data acquisition with Labview, Simulink and/or Matlab.
* Experience in high‑fidelity modelling of rotating thermo‑mechanical systems (e.g. ANSYS, COMSOL, Abaqus).
* Mechatronics integration: Experience with sensor selection and integration (accelerometers, thermocouples, eddy current sensors) within high‑speed rotating machinery.
Further Information
Grade: G7
Work arrangement: Full‑time
Duration: Start date of 1 July 2026 for 18 months
Line manager: CMSE UTC Deputy Director
Our website: https://www.sheffield.ac.uk/eee
We are a Disability Confident Employer.
Closing Date: 12/06/2026
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