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Machine Learning for Advanced Automotive Analytics

About This PhD Project

Project Description

The University of Bradford’s Advanced Automotive Analytics (AAA) research laboratory is an interdisciplinary research unit building on research expertise and track record of the Automotive Research Centre and the Artificial Intelligence Research Group, particularly in automotive reliability and systems engineering modelling, big data and machine learning. Our research explores big data collected from across the product lifecycle to support development of enhanced models and modelling techniques for the management of reliability and design of automotive engineering systems, from personalised powertrain healthcare to pattern recognition and predictive analysis.

Research students joining our dynamic and motivated research team receive training and contribute to multidisciplinary research on computational models and analytics with applications of Big Data in Advanced Automotive Reliability R&D. The main areas of research cover:

- Data and Machine Learning model quality, applicability, analytics, visualisation, pattern recognition for engineering applications;

- Formal description, design and development of machine learning techniques (including statistical, clustering and classification) for engineering data;

- Big Data transformation, visualisation and processing for decision support in engineering applications.

As a PhD student you will work part of the AAA team: PhD and taught students and interns, postdoctoral researchers, academic staff. You will have the opportunity to present your work at conferences and research events; publish contributions in scientific journals; participate in academic and industry activities. The University of Bradford is offering a comprehensive doctoral training programme.


Campean, F., Neagu, D., Doikin, A., Soleimani, M., Byrne, T., & Sherratt, A. (2019). Automotive IVHM: Towards Intelligent Personalised Systems Healthcare. Proceedings of the Design Society: International Conference on Engineering Design, 1(1), 857-866. doi:10.1017/dsi.2019.90

Byrne, T., Doikin, A., Campean, F., & Neagu, D. (2019). An Axiomatic Categorisation Framework for the Dynamic Alignment of Disparate Functions in Cyber-physical Systems. Proceedings of the Design Society: International Conference on Engineering Design, 1(1), 3581-3590. doi:10.1017/dsi.2019.365 Torgunov D., Trundle P., Campean F., Neagu D., Sherratt A. (2020) Vehicle Warranty Claim Prediction from Diagnostic Data Using Classification. In: Ju Z., Yang L., Yang C., Gegov A., Zhou D. (eds) Advances in Computational Intelligence Systems. UKCI 2019. Advances in Intelligent Systems and Computing, vol 1043. Springer, Cham

M Soleimani, F Campean, D Neagu (2018). Reliability Challenges for Automotive Aftertreatment Systems: a State-of-the-art Perspective, Procedia Manufacturing, pp 75-82, Elsevier

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