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.

The EPSRC Centre for Doctoral Training in Statistics and Machine Learning (StatML) will address the EPSRC research priority of the 'physical and mathematical sciences powerhouse' through an innovative cohort-based training program. StatML harnesses the combined strengths of Imperial and Oxford, two world-leading institutions in statistics and machine learning, in collaboration with a broad spectrum of industry partners, to nurture the next generation of leaders in this field. Our students will be at the forefront of advancing the core methodologies of data science and AI, crucial for unlocking the value inherent in data to benefit industry and society. They will be equipped with advanced research, technical, and practical skills, enabling them to make tangible real-world impacts. Our students will be ethical and responsible innovators, championing reproducible research and open science. Collaborating with students, charities and equality experts, StatML will also pioneer a comprehensive strategy to promote inclusivity, attract individuals from diverse backgrounds and eliminate biases. This will help diversify the UK's future statistics and machine learning workforce, essential for ensuring data science is used for public good. Data science and AI are now part of our everyday lives, transforming all sectors of the economy. To future-proof the UK's prosperity and security, it is essential to develop new methodology, specifically tailored to meet the big societal challenges of the future. The techniques underpinning such methods are founded in statistics and machine learning. Through close collaboration with a broad range of industry partners, our cohort-based training will support the UK in producing a critical mass of world-leading researchers with expertise in developing cutting-edge, impactful statistical and machine learning methodology and theory. It is well documented in government and learned society reports that the UK economy has an urgent need for these people. The significant level of industry support for our proposal also highlights the necessity of filling this gap in the UK data science ecosystem. StatML will learn from and build upon our previous successful experiences in cohort training of doctoral students (our existing StatML CDT funded in 2018, as well as other CDTs at Imperial and Oxford). Our students will continue to produce impactful, internationally leading research in statistics and machine learning (as evidenced by our students' impressive publication record and our world-leading research environment, as rated by the REF 2021 evaluation), while complementing this with a bespoke cohort-based Advanced Training program in Statistics and Machine Learning (StatML-AT). StatML-AT has been developed from our experience and in partnership with industry. It will be responsive to emerging technologies and equip our students with the practical skills required to transform how data is used. It will be delivered by our outstanding academics from both institutions alongside with industry leaders to ensure that students receive training in cutting edge technologies, along with the latest ideas in ethics, responsible innovation, sustainability and entrepreneurship. This will be complemented by industrial and academic placements to allow the students to develop their own international network and produce high-impact research. Together, StatML and its partners will train 90+ students over 5 cohorts. More than half of these will be funded from external sources, including 25+ by industry, representing excellent value for money. Our diverse cohorts will benefit from a unique and responsive training program combining academic excellence, industry engagement, and interdisciplinary culture. This will make StatML a vibrant research environment inspiring the next methodological advancements to transform the use of data and AI across industry and society.

Significant challenges lie in the collation, analysis and assessment of data generated to determine the environmental impact assessment (EIA) of products, processes and behaviours throughout the fashion and textiles value chain. The root cause of these problems lies in the absence of standardised test methods when quantifying impact and has led to a lack of industry trust, with many brands developing their own ways of measuring impact. Adding further, is the development of The Green Claims Code in 2021 by the UK government which implements governance to avoid companies making unsubstantiated or inaccurate environmental claims, often leading to accusations of greenwashing. Kering for example, have developed their own Environmental Profit and Loss tool that relies on self-reporting methods of primary data from brands and suppliers to relate impact to financial progress. In comparison, Pangaia in collaboration with Green Story (project partner) adopt a lifecycle analysis (LCA) approach using 13 impact metrics. These diverse measurement methods further add to the blurred boundaries of EIA and prohibits comparability across the industry. Furthermore, data generated is being utilised to inform policy development, industry action and consumer behaviour, meaning reliable, authentic, and useable EIA data is paramount. Critical issues encountered with current EIA methods include: - Collation: data generated being siloed by stages within the value chain resulting in fragmented measures and preventing comparability; methods of data collection relying on self-reporting from brands/suppliers with a lack of verification; accessibility to EIA tools requiring financial buy-in, limiting transparency and data accessibility - Analysis: a lack of standardised test methods to determine and categorise environmental impact; small or limited data sets being scaled up and applied in unsubstantiated contexts; vested interest from funding sources or board members creating biases with the generation and interpretation of data - Assessment: no consideration of the collinearity between measured factors, failing to acknowledge primary, secondary, and tertiary impacts; disparate efforts from stakeholders and disciplines resulting in the lack of collective action; an absence of accepted baselines and thresholds for environmental impact; the invalid use of sustainability scales impeding understanding and comparability In response, the IMPACT+ Network aims to: 1) assemble critical knowledge from the scientific (environmental, forensic and data) and fashion design communities to examine the reliability, authenticity and usability of current EIA methods (e.g., The Higg Index, EU Ecolabel, Good on You); 2) build a world-leading, multi-stakeholder network (brands, manufacturers, retailers, textile recyclers, consumers) to build a greater level of transparency and accuracy in the EIA of products, processes and behaviours. This will be achieved through the delivery of a collaborative programme of activities, across the 24-month project duration and structured across 4 methodological phases (P): P1 - IMPACT+ Symposium; P2 - Impact Analysis; P3 - Discipline Hopping; P4 - Beyond IMPACT+. Central to this will be NetworkPlus funded projects that will explore environmental impact through discipline hopping activities in 4 different areas: materials; manufacturing; consumer use; end-of-life. Critical dialogue between projects and disciplines will develop circular knowledge systems to generate innovative insights and new knowledge. Impact will be generated across 4 critical areas (scholarship, industry, consumers, policy), each contributing to the advancement of knowledge to improve the collation, analysis, and assessment of EIA metrics. This will be reflected in key project outputs including: cross-disciplinary hybrid methodologies; a stakeholder co-created framework; new knowledge demonstrated through publication; the legacy of the IMPACT+ Network.