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Research Fellow in Machine Learning for Advanced Sensor Arrays and Tribology

Vision, Learning and Control

Location:  Highfield Campus
Salary:   £30,395 to £37,345 per annum
Full Time Fixed Term (30 months)
Closing Date:  Thursday 21 February 2019
Interview Date:   Monday 13 May 2019
Reference:  1100919FP

At the Faculty of Engineering and Physical Sciences, we seek to appoint a Research Fellow in Machine Learning to an EPSRC funded project in the domain of machine health monitoring using novel instruments. This project aims to develop next generation electrostatic sensors by miniaturising the existing sensing technology, with embedded electronics, and use array sensors for augmented sensing, and machine learning. The sensor array /learning system would be trained to detect early evidence of lubricated contact decay from charge maps of the surface and allow better prediction of remaining useful life or, what corrective adjustment is needed in running conditions, to assure operational integrity.

The machine learning part of the project will develop novel architectures and corresponding learning algorithms suitable for condition monitoring in a highly dynamic and noisy environment of the mechanical systems and sensor arrays. There will be significant challenges arising from noisy measurements, sensor failures and non-stationarity. Hence the project will not only involve the application of existing algorithms to challenging datasets, but also will push the frontiers of machine learning in the areas of signal processing and novelty detection.

Joining a multidisciplinary team, you will be responsible for the development of novel learning architectures and algorithms. Initial work will involve the application of known methods of signal processing for feature extraction and novelty detection to existing datasets. As the project progresses, more challenging datasets will be available from novel sensors and specific fault inducing scenarios to be developed in the project. The research fellow will be expected to carry out a thorough review of state-of-the-art in relevant topics, implementation and testing of learning algorithms (using high performance computing where necessary), and working in dissemination of research results by co-authoring high quality publications. The applicant should have a relevant PhD in a quantitative discipline such as computer science, mathematics, statistics or engineering, with direct experience in implementing a learning algorithm and testing its performance as part of your PhD work. A PhD in machine learning or statistical inference would be a distinct advantage.

The University of Southampton is an institution in the top one per cent of world universities* and one of the UK’s top 15 research-intensive universities.  We have an international reputation for research, teaching and enterprise and hold an Athena SWAN Silver award.  

You will join a leading Engineering Faculty with an excellent record of collaborative research with industry and academic institutions in the UK and abroad. In September 2013 an Athena SWAN Bronze award was received by the School of Engineering in recognition of our continued commitment to improving equality for women in science and engineering.

Informal enquiries to Professor Mahesan Niranjan m.niranjan@southampton.ac.uk

At the University of Southampton, we value diversity and equality.  

*QS World University Rankings 2012-13

* Applications for Research Fellow positions 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 successful completion of the PhD. Prior to the qualification being awarded, the title of Research Assistant will be assigned.

Application Procedure

You should submit your completed online application form at https://jobs.soton.ac.uk. The application deadline will be midnight on the closing date stated above. If you need any assistance, please call Elsa Samwell (Recruitment Team) on +44 (0) 23 8059 2507. Please quote reference 1100919FP on all correspondence.

The closing date for this job opportunity has now passed, and applications are no longer being accepted for this position

Further details:

We aim to be an equal opportunities employer and welcome applications from all sections of the community. Please note that applications from agencies will not be accepted unless indicated in the job advert.


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