Loughborough University is a top-ten rated university in England for research intensity (REF2014) and an outstanding 66% of the work of Loughborough’s academic staff who were eligible to be submitted to the REF was judged as ‘world-leading’ or ‘internationally excellent’, compared to a national average figure of 43%. In choosing Loughborough for your research, you’ll work alongside academics who are leaders in their field. You will benefit from comprehensive support and guidance from our Doctoral College, including tailored careers advice, to help you succeed in your research and future career. Deep Learning is a disruptive technology that has made remarkable forward leaps in the analysis of large and generic public image datasets. The proposed research will be focused on the entirely new application of Deep Learning to engineering data with a more diverse range of parameters and highly specific domain-knowledge. The research challenge is to devise a procedure to curate data in terms of quality, quantity and meta-data to enable data-fusion, abstraction and generalisation by means of Deep Learning algorithms. This studentship will focus on developing underpinning Deep Learning abstraction architectures to interpret real-time multivariate and heterogenous engineering data. This is a new branch of analytical engineering science that involves a multi-disciplinary working. There are three research components: 1) devise and assemble multivariate and heterogenous engineering datasets for machine learning; 2) develop and deploy Deep Learning intelligence algorithms on the datasets; 3) develop and test visual analytic tools for interactive human-in-the-loop learning processes. This research will lead to entirely new interactive visualization tools to enable exploration of high-dimensional data, scalability of models; model based design, coupled models and distributed datasets. Complimentary Deep Learning algorithms, coupled with interactive visualization, are foreseen to achieve a significant increase in accuracy and abstraction from the combination of multidisciplinary human expertise large amounts of data. The human-machine interaction is essential for Big Data Analytics where raw data is largely un-labelled and un-categorised. Engineering inspired case studies will be used to train and validate the research.