The Collaborative Research & Development (R&D) Partnership project will work with the Fashion Textiles and related Technology (FTT) industry in order develop research-led solutions to business growth, technological and consumer change. This will include working closely with small firms who make up the vast majority (80+%) of the sector, in fashion design, designer-making, manufacturing, retail and in related services that are fed by the fashion & textiles sector, e.g. events, interiors, publishing, performing arts, media and other creative services, as well as a wide range of textiles applications in manufacturing, medical and product design. The research will be delivered by a partnership between several universities led by the University of the Arts London, who together specialise in fashion and textiles design, business, manufacture and marketing, including specialist research centres in sustainable fashion and circular design, sustainable prosperity, materials and textiles manufacturing, in London, Leeds, Loughborough and Cambridge. The R&D project will be based around the East London Fashion & Textiles cluster and the connected production growth corridors of the Thames Gateway and Lea Valley/M11 (London-Cambridge) where opportunities for FTT workspace and manufacturing expansion are evident. The R&D work programme will include short and longer term research projects and enterprise support with small firms/SMEs to identify and develop solutions to the growth of their business, products and markets and related skills needs; work with larger fashion brands to develop more sustainable products through innovative design, manufacture and waste processing; research consumer experience and needs in material/fashion brands and retailing, including the future place of high street retail, store design and online markets; test new and existing synthetic and natural materials for new product development; and explore markets for more sustainable UK fibres/chemical processes and opportunities for regional UK textile production. The R&D programme, which will be co-designed with FTT companies and industry associations, will also identify the related skill and training needs which accompany the economic and technological challenges facing the FTT industry, and design through the university partners and other training providers (e.g. FE Colleges) and enterprise support organisations, new and novel training and Continuing Professional Development programmes.

The CDT will train the next generation of leaders in statistics and statistical machine learning, who will be able to develop widely-applicable novel methodology and theory, as well as create application-specific methods, leading to breakthroughs in real-world problems in government, medicine, industry and science. The research will focus on the development of applicable modern statistical theory and methods as well as on the underpinnings of statistical machine learning. The research will be strongly linked to applications. There is an urgent national need for graduates from this CDT. Large volumes of complicated data are now routinely collected in all sectors of society, encompassing electronic health records, massive scientific datasets, governmental data, and data collected through the advent of the digital economy. The underpinning techniques for exploiting these data come from statistics and machine learning. Exploiting such data is crucial for future UK prosperity. However, several reports from government and learned societies have identified a lack of individuals able to exploit this data. In many situations, existing methodology is insufficient. Off-the-shelf approaches may be misleading due to a lack of reproducibility or sampling biases which they do not correct. Furthermore, understanding the underlying mechanisms is often desired: scientifically valid, interpretable and reproducible results are needed to understand scientific phenomena and to justify decisions, particularly those affecting individuals. Bespoke, model-based statistical methods are needed, that may need to be blended with statistical machine learning approaches to deal with large data. Individuals that can fulfill these more sophisticated demands are doctoral level graduates in statistics who are well versed in the foundations of machine learning. Yet the UK only graduates a small number of statistics PhDs per year, and many of these graduates will not have been exposed to machine learning. The Centre will bring together Imperial and Oxford, two top statistics groups, as equal partners, offering an exceptional training environment and the direct involvement of absolute research leaders in their fields. The supervisor pool will include outstanding researchers in statistical methodology and theory as well as in statistical machine learning. We will use innovative and student-led teaching, focussing on PhD-level training. Teaching cuts across years and thus creates strong cohort cohesion not just within a year group but also between year groups. We will link theoretical advances to application areas through partner interactions as well as through a placement of students with users of statistics. The CDT has a large number of high profile partners that helped shape our application priority areas (digital economy, medicine, engineering, public health, science) and that will co-fund and co-supervise PhD students, as well as co-deliver teaching elements.