POETS (Partially Ordered Event Triggered Systems) is a significantly different way of approaching large, compute intensive problems. The evolution of traditional computer technology has taken us from simple machines with a handful of bytes of memory and (by the standards of today) glacial clock speeds, to multi-gigabyte architectures running five or six orders of magnitude faster, but with the same fundamental process at the heart: a central core doing one thing at a time. Over the past few years, architectures have appeared containing multiple cores, but exploiting these efficiently in the general case remains a 'holy grail' of computer science. POETS takes an alternative approach, made possible only today by the proliferation of cheap, small cores and massive reconfigurable platforms. A previous EPSRC project, BIMPA, enabled us to assemble a million core machine, creating a kind of 'meta-computer'. Rather than program explicitly the behaviour of each core and each communication between them, as is done in conventional supercomputers, here the programmer defines a set of relatively small, simple behaviours for the set of cores, and leaves them to get on with it - with the right behavioural definitions , the system 'self-organises' to produce the desired results. BIMPA was designed primarily for neuroscience applications, but a subsidiary research objective allowed us to study the use of the architecture for alternative (physics-based) problems, and we have demonstrated that this kind of approach can lead to dramatic speed increases over conventional solution techniques. POETS is not a general-purpose computing technique, but it is elegantly suited to a variety of traditionally compute intensive engineering and research problems, where it can produce results orders of magnitude faster than conventional machines at a fraction of the cost. The purpose of this research project is to explore this application arena: what kind of architectures are best (fastest)? How might they be automatically configured to self-organise? How might we build bridges between this new technology and a nascent user base? Industry has invested heavily - quite sensibly - in computing technology over the years, and if POETS is to become the disruptive technology we believe it to be capable of, we need to address a serious 'hearts and minds' issue for commercial uptake to ensue.

It is now clear that biological functions or diseases arise from complex interactive networks operating on many different scales. The translational work needed to transform promising new drugs and therapies into commercial products will increasingly require predictive mathematical and computational modelling at the systems level. The Systems Approaches to Biomedical Science (SABS) Centre aims to meet this demand by training a new generation of responsive research leaders with the ability to generate and apply novel physical and mathematical techniques to solve research problems of relevance to the pharmaceutical, biomedical, biotechnology and related sectors. SABS will address these industry-relevant scientific questions from the real world, and explore them through genuine academic-industrial collaborations. SABS will provide training and research across a wide range of areas, including the design and testing of new chemical and biological entities, modelling biological systems, and robust analysis of complex datasets. Such cross-disciplinary work will introduce students to cutting edge organic chemistry, chemoinformatics, chemical and synthetic biology, biophysics, advanced computational simulation, bioinformatics, data mining, statistical analysis, physical and structural study of biomolecules, and mathematical modelling. Over the last 4 years the SABS team have created a wide network of contacts within Oxford and across industry. SABS will continue to work closely with its partner companies (AstraZeneca, Diamond Light Source, e-Therapeutics, Evotec, GE Healthcare, GlaxoSmithKline, Hoffmann LaRoche, InhibOx, Lilly UK, Microsoft, Novartis, Pfizer, Structural Genomics Consortium and UCB), and with 14+ departments across the University. Every SABS student will be co-supervised and co-funded by industry and will be fully exposed to the industrial context of their research in both the taught programme, and in their industry-based research projects. They will develop skills in project management, strategic planning, leadership, team working, commercial awareness, and problem solving all of which will be required to translate innovations in basic and medical science into commercial product development. SABS will continue to use its ground-breaking and, currently, unique Open Innovation IP agreement, which allows all participants in the SABS consortium to see the results of all research projects. Participating companies regard it as a trail-blazing model for the future of industry-academia collaboration, because it simplifies inter-company research collaboration within the consortium and improves the existing business process for innovation and academic collaborations. From an academic perspective, it allows the students to participate in impactful industrial research whilst still gaining the benefits of research discussion with their peer cohort. Oxford University has made substantial investments both in infrastructure for graduate training and all research areas associated with SABS. It actively promotes interdisciplinary research with external collaborators, and is currently investing heavily in the new Target Discovery and Big Data institutes. SABS has demonstrated very strong user pull, and an ability to recruit new companies; three organisations are currently in the process of joining. In this new bid the companies have doubled their cash contribution per student to £30k, and will also cover all associated research and travel costs (currently averaging £8k per student); a clear commitment to the continuation of the SABS centre. Our minimum cohort size of 14 means industry will make a minimum cash contribution to student funding of £2.1m and a further £560k to research costs.

Cloud computing offers the ability to acquire vast, scalable computing resources on-demand. It is revolutionising the way in which data is stored and analysed. The dynamic, scalable approach to analysis offered by cloud computing has become important due to the growth of "big data": the large, often complex, datasets now being created in almost all fields of activity, from healthcare to e-commerce. Unfortunately, due to a lack of expertise, the full potential of cloud computing for extracting knowledge from big data has rarely been achieved outside a few large companies; as a result, many organisations fail to realize their potential to be transformed through extracting more value from the data available to them. UK industry faces a huge skills gap in this area as the demand for big data staff has risen exponentially (912%) over the past five years from 400 advertised vacancies in 2007 to almost 4,000 in 2012 (e-skills UK, Jan 2013). In addition, the demand for big data skills will continue to outpace the demand for standard IT skills, with big data vacancies forecast to increase by around 18% per annum in comparison with 2.5% for IT. Over the next five years this equates to a 92% rise in the demand for big data skills with around 132K new jobs being created in the UK (e-skills UK, Jan 2013). While characteristics such as size, data dependency and the nature of business activity will affect the potential for organisations to realise business benefits from big data, organisations don't have to be big to have big data issues. The problems and benefits are as true for many SMEs as they are for big business which, inevitably broadens and increases the demand for cloud and big data skills. Further, even when security concerns prevent the use of external "public" clouds for certain types of data, organisations are applying the same approaches to their own internal IT resources, using virtualisation to create "private" clouds for data analysis. Addressing these challenges requires expert practitioners who can bridge between the design of scalable algorithms, and the underlying theory in the modelling and analysis of data. It is perhaps not surprising that these skills are in short supply: traditional undergraduate and postgraduate courses produce experts in one or the other of these areas, but not both. We therefore propose to create a multi-disciplinary CDT to fill this significant gap. It will produce multi-disciplinary experts in the mathematics, statistics and computing science of extracting knowledge from big data, with practical experience in exploiting this knowledge to solve problems across a range of application domains. Based on a close collaboration between the School of Computing Science and the School of Mathematics and Statistics at Newcastle University, the CDT will address market requirements and overcome the existing skills barriers. The student intake will be drawn from graduates in computing science, mathematics and statistics. Initial training will provide the core competencies that the students will require, before they collaborate in group projects that teach them to address real research challenges drawn from application domains, before moving on to their individual PhD topic. The PhD topics will be designed to allow the students to focus deeply on a real-world problem the solution of which requires an advance in the underlying computing, maths and statistics. To reinforce this focus, they will spend time on a placement hosted by an industrial or applied academic partner facing that problem. Their PhD research will therefore deepen their knowledge of the field and teach them how to exploit it to solve challenging problems. Working in the new, custom-designed Cloud Innovation Centre, the students will derive continuous benefit from being co-located with researchers, industry experts, and their fellow students; immersing them in a group with a wide range of skills, knowledge and experiences.

Building upon our existing flagship industry-linked EPSRC & MRC CDT in Systems Approaches to Biomedical Science (SABS), the new EPSRC CDT in Sustainable Approaches to Biomedical Science: Responsible and Reproducible Research - SABS:R^3 - will train a further five cohorts, each of 15 students, in cutting-edge systems approaches to biomedical research and, uniquely within the UK, in advanced practices in software engineering. Our renewed goal is to bring about a transformation of the research culture in computational biomedical science. Computational methods are now at the heart of biomedical research. From the simulation of the behaviour of complex systems, through the design and automation of laboratory experiments, to the analysis of both small and large-scale data, well-engineered software has proved capable of transforming biomedical science. Biomedical science is therefore dependent as never before on research software. Industries reliant on this continued innovation in biomedical science play a critical role in the UK economy. The biopharmaceutical and medical technology industrial sectors alone generate an annual turnover of over £63 billion and employ 233,000 scientists and staff. In his foreword to the 2017 Life Sciences Industrial Strategy, Sir John Bell noted that, "The global life sciences industry is expected to reach >$2 trillion in gross value by 2023... there are few, if any, sectors more important to support as part of the industrial strategy." The report identifies the need to provide training in skills in "informatics, computational, mathematical and statistics areas" as being of major concern for the life sciences industry. Over the last 9 years, the existing SABS CDT has been working with its consortium of now 22 industrial and institutional partners to meet these training needs. Over this same period, continued advances in information technology have accelerated the shift in the biomedical research landscape in an increasingly quantitative and predictive direction. As a result, computational and hence software-driven approaches now underpin all aspects of the research pipeline. In spite of this central importance, the development of research software is typically a by-product of the research process, with the research publication being the primary output. Research software is typically not made available to the research community, or even to peer reviewers, and therefore cannot be verified. Vast amounts of research time is lost (usually by PhD students with no formal training in software development) in re-implementing already-existing solutions from the literature. Even if successful, the re-implemented software is again not released to the community, and the cycle repeats. No consideration is made of the huge benefits of model verification, re-use, extension, and maintainability, nor of the implications for the reproducibility of the published research. Progress in biomedical science is thus impeded, with knock-on effects into clinical translation and knowledge transfer into industry. There is therefore an urgent need for a radically different approach. The SABS:R^3 CDT will build on the existing SABS Programme to equip a new generation of biomedical research scientists with not only the knowledge and methods necessary to take a quantitative and interdisciplinary approach, but also with advanced software engineering skills. By embedding this strong focus on sustainable and open computational methods, together with responsible and reproducible approaches, into all aspects of the new programme, our computationally-literate scientists will be equipped to act as ambassadors to bring about a transformation of biomedical research.

This Centre for Doctoral training in Industrially Focused Mathematical Modelling will train the next generation of applied mathematicians to fill critical roles in industry and academia. Complex industrial problems can often be addressed, understood, and mitigated by applying modern quantitative methods. To effectively and efficiently apply these techniques requires talented mathematicians with well-practised problem-solving skills. They need to have a very strong grasp of the mathematical approaches that might need to be brought to bear, have a breadth of understanding of how to convert complex practical problems into relevant abstract mathematical forms, have knowledge and skills to solve the resulting mathematical problems efficiently and accurately, and have a wide experience of how to communicate and interact in a multidisciplinary environment. This CDT has been designed by academics in close collaboration with industrialists from many different sectors. Our 35 current CDT industrial partners cover the sectors of: consumer products (Sharp), defence (Selex, Thales), communications (BT, Vodafone), energy (Amec, BP, Camlin, Culham, DuPont, GE Energy, Infineum, Schlumberger x2, VerdErg), filtration (Pall Corp), finance (HSBC, Lloyds TSB), food and beverage (Nestle, Mondelez), healthcare (e-therapeutics, Lein Applied Diagnostics, Oxford Instruments, Siemens, Solitonik), manufacturing (Elkem, Saint Gobain), retail (dunnhumby), and software (Amazon, cd-adapco, IBM, NAG, NVIDIA), along with two consultancy companies (PA Consulting, Tessella) and we are in active discussion with other companies to grow our partner base. Our partners have five key roles: (i) they help guide and steer the centre by participating in an Industrial Engagement Committee, (ii) they deliver a substantial elements of the training and provide a broad exposure for the cohorts, (iii) they provide current challenges for our students to tackle for their doctoral research, iv) they give a very wide experience and perspective of possible applications and sectors thereby making the students highly flexible and extremely attractive to employers, and v) they provide significant funding for the CDT activities. Each cohort will learn how to apply appropriate mathematical techniques to a wide range of industrial problems in a highly interactive environment. In year one, the students will be trained in mathematical skills spanning continuum and discrete modelling, and scientific computing, closely integrated with practical applications and problem solving. The experience of addressing industrial problems and understanding their context will be further enhanced by periods where our partners will deliver a broad range of relevant material. Students will undertake two industrially focused mini-projects, one from an academic perspective and the other immersed in a partner organisation. Each student will then embark on their doctoral research project which will allow them to hone their skills and techniques while tackling a practical industrial challenge. The resulting doctoral students will be highly sought after; by industry for their flexible and quantitative abilities that will help them gain a competitive edge, and by universities to allow cutting-edge mathematical research to be motivated by practical problems and be readily exploitable.