The Automotive Research Centre has a strong track record of collaborations with the global automotive industry focussed on the modelling and optimisation of complex automotive systems for reliability and robustness. We have set up the interdisciplinary Advanced Automotive Analytics research laboratory with industry partnership and funding, to develop the next generation algorithms and decision support systems for intelligent personalised vehicle healthcare. Our approach, underpinned by the concept of an Automotive Analytics Factory, is to leverage rich engineering data sources available from across the systems lifecycle (design and development, manufacturing, use and retirement) to generate actionable insight from advanced systems diagnostics and prognostics. This is underpinned by a framework for knowledge-enabled machine learning, that integrates engineering knowledge with the data-driven machine learning models, to deliver high efficiency diagnostics and prognostics systems for complex systems application. The power of this approach has already been demonstrated with case studies conducted with our industrial partners.
We are now looking to further developments in this research, in particular to focus on knowledge extraction and integration across the design, manufacturing and operation phases. We are particularly interested in the integration of model based methods and causal reasoning modelling for the behaviour of the systems (as a behaviour centric digital twin), with data-driven modelling of driver, driving and system behaviour, focussed on extraction of tacit knowledge from complex data streams, to continuously update the system models. We are focussing on end-user applications including knowledge update for system design, data-driven modelling and simulation, and intelligent verification and validation, as well as integrated health management.
The Advanced Automotive Analytics provides a vibrant interdisciplinary environment, offering rich opportunities to interact with industry stakeholders (global OEMs) providing cases studies with real world data and problem scenarios.
This is a self-funded project.
We welcome applications from individual with a strong interest for data science methods applied in an automotive engineering systems modelling context.