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.

The UK is a global leader in health research and healthcare technology. It is one of the most important sectors in our economy, and the largest in terms of commercial expenditure on research and development. It is also critical to the future of our health service: we need new ways of diagnosing and treating illness, new ways of delivering care, and new ways of planning for and dealing with challenges such as the recent pandemic. To maintain this leading position, the UK needs more healthcare data scientists. It needs data scientists who can advance the state of the art in computer science, statistics, and engineering in support of health and healthcare transformation. These scientists need to have an excellent understanding of the application domain: that is, they need to understand the fundamental features of health data, how to manipulate and model it, draw conclusions from it, and explain the resulting insights to stakeholders. They need also to know how to behave responsibly and ethically. For example, the methods and tools that they produce, and the research that they conduct, should take proper account of the variation and diversity in our population. Above all, they need to know how to work effectively with people from different backgrounds: health professionals, health researchers from academia and industry, patients, and the public. The Oxford EPSRC CDT in Healthcare Data Science will provide the research training that turns talented science graduates into this kind of data scientist. Supporting the EPSRC strategic delivery plan in the research priority area of transforming health and healthcare, it will work in partnership with the NHS, with the UK Health Security Agency, and with a range of research groups and organisations in academia and industry, ensuring that students obtain the essential combination of scientific rigour and real-world experience. The training programme is cohort-based, meaning that students learn how to work together and support one another. This is essential feature: the challenges that we face can only be addressed through trust and collaboration. The programme is designed to be accessible to graduates in different subjects, and we will make efforts to ensure that the cohorts are diverse and representative of the UK. Our experience with the existing EPSRC CDT in Health Data Science shows that this approach works very well. Our students have developed new approaches using real data to solve important problems, and to deliver real benefit in terms of health and healthcare transformation.