Neurotechnology is the use of insights and tools from mathematics, physics, chemistry, biology and engineering to investigate neural function and treat dysfunction; and additionally, the development of novel technology inspired by neuroscience. Brain-related illnesses affect more than two billion people worldwide, and add an annual burden which has been estimated to exceed $US 2.2 trillion. This is exacerbated by the aging societal demographic in most industrialized nations, including the UK: many brain disorders, such as dementia, are closely linked to age. There is a real need to solve this problem before it becomes an impossible burden for the economy to carry. The Centre for Doctoral Training in Neurotechnology for Life and Health will train a unique cadre of multidisciplinary researchers, who will combine an understanding of their neuroscience problem with skills in technology development, to make groundbreaking advances in our ability to treat brain disorders and to improve the quality of life and health in the UK. There is a strong need for such a pool of researchers in the UK now. Advances in treatments for brain disorders have to date relied largely upon a purely pharmaceutical approach, however the development of completely new drugs has slowed to a trickle as we have run into the "wall of complexity" where the cost of finding new drugs which do not have intolerable side effects becomes insurmountable. "High throughput" approaches have only pushed this wall back a year or two - as Peter Mueller of Vertex commented to us, "we need to shift our thinking from high throughput to high content". Our industry partners have emphasized to us that a new, engineering-driven approach is needed, to develop new solutions for uncovering that content. A key driver behind the development of this CDT bid has been the need for PhD level graduates with a multidisciplinary training, who bring with them both a detailed understanding of a translational neuroscience question, and the strong background in technology development needed to develop solutions. Our industry partners have all emphasized that the lack of availability of such researchers is currently a major limiting factor in their development prospects. By addressing this skills shortage, the CDT will have a major long-term impact on our ability to intervene in brain disorders, enhancing both academic and industrial research efforts to find solutions. "There is an unmet requirement for PhD graduates with a combined expertise in engineering and neuroscience and the proposed CDT in Neurotechnology will help to address this shortage" Jonas Gårding, Research & Physics Director Neuroscience, Elekta Instrument AB "The program that you propose to develop at the interface of neuroscience and engineering will produce PhD graduates with the potential to make major contributions to our research objectives" Kris Famm, PhD, VP Bioelectronics R&D, GlaxoSmithKline "We believe that the research conducted at the centre will have the potential to have a significant impact on the Parkinson's research field and ultimately on the lives of Parkinson's patients" Dr Kieran Breen, Director of Research and Innovation, Parkinson's UK.

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.