Our EPSRC CDT in Advanced Engineering for Personalised Surgery & Intervention will train a new generation of researchers for diverse engineering careers that deliver patient and economic impact through innovation in surgery & intervention. We will achieve this through cohort training that implements the strategy of the EPSRC by working across sectors (academia, industry, and NHS) to stimulate innovations by generating and exchanging knowledge. Surgery is recognised as an "indivisible, indispensable part of health care" but the NHS struggles to meet its rising demand. More than 10m UK patients underwent a surgical procedure in 2021, with a further 5m patients still requiring treatment due to the COVID-19 backlog. This level of activity, encompassing procedures such as tumour resection, reconstructive surgery, orthopaedics, assisted fertilisation, thrombectomy, and cardiovascular interventions, accounts for a staggering 10% of the healthcare budget, yet it is not always curative. Unfortunately, one third of all country-wide deaths occur within 90 days of surgery. The Department of Health and Social Care urges for "innovation and new technology", echoing the NHS Long Term Plan on digital transformation and personalised care. Our proposed CDT will contribute to this mission and deliver mission-inspired training in the EPSRC's Research Priority "Transforming Health and Healthcare". In addition to patient impact, engineering innovation in surgery and intervention has substantial economic potential. The UK is a leader in the development of such technology and the 3rd biggest contributor to Europe's c.150bn euros MedTech market (2021). The market's growth rate is substantial, e.g., an 11.4% (2021 - 2026) compound annual growth rate is predicted just for the submarket of interventional robotics. The engineering scientists required to enhance the UK's societal, scientific, and economic capacity must be expert researchers with the skills to create innovative solutions to surgical challenges, by carrying out research, for example, on micro-surgical robots for tumour resection, AI-assisted surgical training, novel materials and theranostic agents for "surgery without the knife", and predictive computational models to develop patient-specific surgical procedures. Crucially, they should be comfortable and effective in crossing disciplines while being deeply engaged with surgical teams to co-create technology solutions. They should understand the pathway from bench-to-bedside and possess an entrepreneurial mindset to bring their innovations to the market. Such researchers are currently scarce, making their training a key contributor to the success of the UK Government's "Build Back Better - our plan for growth" and UKRI's "five-year strategy". The cross-discipline collaboration of King's School of Biomedical Engineering & Imaging Sciences (BMEIS, host), Department of Engineering, and King's Health Partners (KHP), our Academic Health Science Centre, will create an engineering focused CDT that embeds students within three acute NHS Trusts. Our CDT brings together 50+ world-class supervisors whose grant portfolio (c.£150m) underpins the full spectrum of the CDT's activity, i.e., Smart Instruments & Active Implants, Surgical Data Science, and Patient-specific Modelling & Simulation. We will offer MRes/PhD training pathway (1+3), and direct PhD training pathway (0+4). All students, regardless of pathway, will benefit from continuous education modules which cover aspects of clinical translation and entrepreneurship (with King's Entrepreneurship Institute), as well as core value modules to foster a positive research culture. Our graduates will acquire an entrepreneurial mindset with skills in data science, fundamental AI, computational modelling, and surgical instrumentation and implants. Career paths will range from creating next generation medical innovators within academia and/or industry to MedTech start-up entrepreneurs.

SOCIAM - Social Machines - will research into pioneering methods of supporting purposeful human interaction on the World Wide Web, of the kind exemplified by phenomena such as Wikipedia and Galaxy Zoo. These collaborations are empowering, as communities identify and solve their own problems, harnessing their commitment, local knowledge and embedded skills, without having to rely on remote experts or governments. Such interaction is characterised by a new kind of emergent, collective problem solving, in which we see (i) problems solved by very large scale human participation via the Web, (ii) access to, or the ability to generate, large amounts of relevant data using open data standards, (iii) confidence in the quality of the data and (iv) intuitive interfaces. "Machines" used to be programmed by programmers and used by users. The Web, and the massive participation in it, has dissolved this boundary: we now see configurations of people interacting with content and each other, typified by social web sites. Rather than dividing between the human and machine parts of the collaboration (as computer science has traditionally done), we should draw a line around them and treat each such assembly as a machine in its own right comprising digital and human components - a Social Machine. This crucial transition in thinking acknowledges the reality of today's sociotechnical systems. This view is of an ecosystem not of humans and computers but of co-evolving Social Machines. The ambition of SOCIAM is to enable us to build social machines that solve the routine tasks of daily life as well as the emergencies. Its aim is to develop the theory and practice so that we can create the next generation of decentralised, data intensive, social machines. Understanding the attributes of the current generation of successful social machines will help us build the next. The research undertakes four necessary tasks. First, we need to discover how social computing can emerge given that society has to undertake much of the burden of identifying problems, designing solutions and dealing with the complexity of the problem solving. Online scaleable algorithms need to be put to the service of the users. This leads us to the second task, providing seamless access to a Web of Data including user generated data. Third, we need to understand how to make social machines accountable and to build the trust essential to their operation. Fourth, we need to design the interactions between all elements of social machines: between machine and human, between humans mediated by machines, and between machines, humans and the data they use and generate. SOCIAM's work will be empirically grounded by a Social Machines Observatory to track, monitor and classify existing social machines and new ones as they evolve, and act as an early warning facility for disruptive new social machines. These lines of interlinked research will initially be tested and evaluated in the context of real-world applications in health, transport, policing and the drive towards open data cities (where all public data across an urban area is linked together) in collaboration with SOCIAM's partners. Putting research ideas into the field to encounter unvarnished reality provides a check as to their utility and durability. For example the Open City application will seek to harness citywide participation in shared problems (e.g. with health, transport and policing) exploiting common open data resources. SOCIAM will undertake a breadth of integrated research, engaging with real application contexts, including the use of our observatory for longitudinal studies, to provide cutting edge theory and practice for social computation and social machines. It will support fundamental research; the creation of a multidisciplinary team; collaboration with industry and government in realization of the research; promote growth and innovation - most importantly - impact in changing the direction of ICT.

Medical imaging has transformed clinical medicine in the last 40 years. Diagnostic imaging provides the means to probe the structure and function of the human body without having to cut open the body to see disease or injury. Imaging is sensitive to changes associated with the early stages of cancer allowing detection of disease at a sufficient early stage to have a major impact on long-term survival. Combining imaging with therapy delivery and surgery enables 3D imaging to be used for guidance, i.e. minimising harm to surrounding tissue and increasing the likelihood of a successful outcome. The UK has consistently been at the forefront of many of these developments. Despite these advances we still do not know the most basic mechanisms and aetiology of many of the most disabling and dangerous diseases. Cancer survival remains stubbornly low for many of the most common cancers such as lung, head and neck, liver, pancreas. Some of the most distressing neurological disorders such as the dementias, multiple sclerosis, epilepsy and some of the more common brain cancers, still have woefully poor long term cure rates. Imaging is the primary means of diagnosis and for studying disease progression and response to treatment. To fully achieve its potential imaging needs to be coupled with computational modelling of biological function and its relationship to tissue structure at multiple scales. The advent of powerful computing has opened up exciting opportunities to better understand disease initiation and progression and to guide and assess the effectiveness of therapies. Meanwhile novel imaging methods, such as photoacoustics, and combinations of technologies such as simultaneous PET and MRI, have created entirely new ways of looking at healthy function and disturbances to normal function associated with early and late disease progression. It is becoming increasingly clear that a multi-parameter, multi-scale and multi-sensor approach combining advanced sensor design with advanced computational methods in image formation and biological systems modelling is the way forward. The EPSRC Centre for Doctoral Training in Medical Imaging will provide comprehensive and integrative doctoral training in imaging sciences and methods. The programme has a strong focus on new image acquisition technologies, novel data analysis methods and integration with computational modelling. This will be a 4-year PhD programme designed to prepare students for successful careers in academia, industry and the healthcare sector. It comprises an MRes year in which the student will gain core competencies in this rapidly developing field, plus the skills to innovate both with imaging devices and with computational methods. During the PhD (years 2 to 4) the student will undertake an in-depth study of an aspect of medical imaging and its application to healthcare and will seek innovative solutions to challenging problems. Most projects will be strongly multi-disciplinary with a principle supervisor being a computer scientist, physicist, mathematician or engineer, a second supervisor from a clinical or life science background, and an industrial supervisor when required. Each project will lie in the EPSRC's remit. The Centre will comprise 72 students at its peak after 4 years and will be obtaining dedicated space and facilities. The participating departments are strongly supportive of this initiative and will encourage new academic appointees to actively participate in its delivery. The Centre will fill a significant skills gap that has been identified and our graduates will have a major impact in academic research in his area, industrial developments including attracting inward investment and driving forward start-ups, and in advocacy of this important and expanding area of medical engineering.

SOCIAM - Social Machines - will research into pioneering methods of supporting purposeful human interaction on the World Wide Web, of the kind exemplified by phenomena such as Wikipedia and Galaxy Zoo. These collaborations are empowering, as communities identify and solve their own problems, harnessing their commitment, local knowledge and embedded skills, without having to rely on remote experts or governments. Such interaction is characterised by a new kind of emergent, collective problem solving, in which we see (i) problems solved by very large scale human participation via the Web, (ii) access to, or the ability to generate, large amounts of relevant data using open data standards, (iii) confidence in the quality of the data and (iv) intuitive interfaces. "Machines" used to be programmed by programmers and used by users. The Web, and the massive participation in it, has dissolved this boundary: we now see configurations of people interacting with content and each other, typified by social web sites. Rather than dividing between the human and machine parts of the collaboration (as computer science has traditionally done), we should draw a line around them and treat each such assembly as a machine in its own right comprising digital and human components - a Social Machine. This crucial transition in thinking acknowledges the reality of today's sociotechnical systems. This view is of an ecosystem not of humans and computers but of co-evolving Social Machines. The ambition of SOCIAM is to enable us to build social machines that solve the routine tasks of daily life as well as the emergencies. Its aim is to develop the theory and practice so that we can create the next generation of decentralised, data intensive, social machines. Understanding the attributes of the current generation of successful social machines will help us build the next. The research undertakes four necessary tasks. First, we need to discover how social computing can emerge given that society has to undertake much of the burden of identifying problems, designing solutions and dealing with the complexity of the problem solving. Online scaleable algorithms need to be put to the service of the users. This leads us to the second task, providing seamless access to a Web of Data including user generated data. Third, we need to understand how to make social machines accountable and to build the trust essential to their operation. Fourth, we need to design the interactions between all elements of social machines: between machine and human, between humans mediated by machines, and between machines, humans and the data they use and generate. SOCIAM's work will be empirically grounded by a Social Machines Observatory to track, monitor and classify existing social machines and new ones as they evolve, and act as an early warning facility for disruptive new social machines. These lines of interlinked research will initially be tested and evaluated in the context of real-world applications in health, transport, policing and the drive towards open data cities (where all public data across an urban area is linked together) in collaboration with SOCIAM's partners. Putting research ideas into the field to encounter unvarnished reality provides a check as to their utility and durability. For example the Open City application will seek to harness citywide participation in shared problems (e.g. with health, transport and policing) exploiting common open data resources. SOCIAM will undertake a breadth of integrated research, engaging with real application contexts, including the use of our observatory for longitudinal studies, to provide cutting edge theory and practice for social computation and social machines. It will support fundamental research; the creation of a multidisciplinary team; collaboration with industry and government in realization of the research; promote growth and innovation - most importantly - impact in changing the direction of ICT.

The greatest advance in magnetic technology in the last 20 years has been the development of "nanomagnetic" devices, magnetic systems with dimensions as small as ten billionths of a metre. The most common examples of this are found in computer hard-disk drives, where both the storage media and the sensors used to read data back are nanomagnetic in nature. The prevalence of modern personal computers means that the vast majority of homes and businesses in the United Kingdom, and indeed in much of the developed world, are now in some way dependent on nanomagnetic technology. Many other nanomagnetic devices are also being developed including magnetic memory devices, magnetic logic devices, microwave resonators, devices for medical diagnostics and magnetic sensors. These new technologies have the potential to be faster, cheaper and more efficient than their existing counterparts. For example, non-volatile magnetic memory chips will allow personal computers to be booted up into the exact state they were in prior to being shut down, removing the necessity of leaving systems switched on over extended periods. Similarly, magnetic bio-chips will soon allow complex medical tests to be performed at the doctor's surgery rather than in a laboratory, and at a faction of the price. In nanomagnetic systems understanding the effect of finite temperature is of critical importance, as thermal effects introduce disorder making it impossible to predict exactly how a device will behave. In hard-disks thermal excitations can cause data to be lost by reversing the individual "bits" that make up a file. This phenomenon is the primary factor that restricts the capacity of modern hard-disks. In other technologies the randomising effects of thermal perturbations make devices unreliable by making it impossible to predict the exact state a device will be in before and after an external operation is performed. Again, this lack of reliability is a leading factor in preventing new nanomagnetic technologies, and the social and environmental benefits they will bring, being available on the high street. Despite the huge technological importance of these "stochastic" effects they are poorly understood with most studies considering them only in a phenomenological or empirical fashion. To be able to understand and accurately predict stochastic behaviour in magnetic systems it is necessary to have a thorough knowledge of two parameters: the energy barrier, which determines how strongly a system is confined to a given state; and the attempt frequency, which determines how often thermal excitations try to alter the configuration of a system. Unfortunately neither of these parameters are accessible by standard measurement techniques, and hence they are neither well understood, nor characterised. In this fellowship I will use time, frequency and temperature resolved measurements, coupled with new numerical modelling techniques, to directly measure both attempt frequencies and energy barriers across a broad range of technologically relevant magnetic systems. These will include those for use in new hard-disk technologies, memory devices, information processing systems, novel sensors and microwave resonators. In doing this I will create the first comprehensive framework with which to a) understand, b) predict and c) mitigate the effects of stochastic behaviour in nanomagnetic devices. This will allow researchers and technologists to, at last, quantitatively predict how thermal perturbations will affect nanomagnetic devices, and understand how the problems they introduce can be overcome. There is currently an explosion of interest in developing new nanomagnetic technologies in both academia and in industry. This fellowship will be critical to ensuring that progress is not inhibited by a lack of understanding of stochastic magnetic behaviour, and that the great potential of nanomagnetic technology is brought to the high street.