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EPSRC 2018 CDT Outline Results

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EPSRC 2018 CDT Outline Results EPSRC 2018 CDT Outline Results The applicants of the proposed Centres detailed below are invited to submit a full CDT proposal to the next stage of the funding exercise following successful assessment of the outline application. Applications were consider across seven expert panels. This list combines all invited applications, listed in Grant Reference order. No inference of ranking should be made. Grant Reference Applicant Organisation Grant Title Treleaven, University College EPSRC CENTRE for DOCTORAL TRAINING in Data-Driven Research and EP/S005668/1 Professor P London Impact Government, industry and society are experiencing a Data-Driven 'revolution', as profound as the Industrial Revolution. The UK's success in Data-Driven research will be pivotal for efficient public services, competitive industries, new start-ups, high employment levels and a fairer society. Given the importance of Data-Driven research, UCL has co-created together with our Public sector and Services sectors (i.e., 'industry') partners this major (20 students pa) Computer Science-led, UCL-wide, interdisciplinary CDT in Data-Driven research and impact, that uniquely combines fundamental, applied, and human-centred research, and a unique enterprise programme. Elliott, Professor University of EPSRC Centre for Doctoral Training in Computational Methods for Materials EP/S005676/1 JA Cambridge Science Moore's law states that the number of active components on a microchip doubles every 18 months. Variants of this observation can be applied to many measures of computer performance, such as memory and hard disk capacity, and to reductions in the cost of computations. This remarkable rise of computational power has affected all of our lives in profound ways, through the widespread usage of computers, the internet and portable electronic devices, such as smartphones and tablets. However, in the light of recent targeted attacks on performance-enhancing hardware design flaws, such as Meltdown and Spectre, and the encroaching limits of quantum mechanics on further miniaturisation of chip components, it appears that in future significant gains in computing performance will have to come from improvements in software engineering and the development of new algorithms. Computer software plays an important role in enhancing computational performance and in many cases it has been found that for every factor of 10 increase in computational performance achieved by faster hardware, improved software has further increased computational performance by a factor of 100. Furthermore, improved software is also essential for extending the range of physical properties and processes which can be studied computationally. The existing EPSRC Centre for Doctoral Training in Computational Methods for Materials Science provides training in numerical methods and modern software development techniques so that the students in the CDT are capable of developing innovative new software which can be used, for instance, to help design new materials and understand the complex processes that occur in materials. It has received over £2 million of investment from industrial and charitable sources to supplement the tax payer contributions. The UK, and in particular Cambridge, has been a Page 1 of 183 pioneer in both software and hardware since the earliest programmable computers, and through this further strategic invetment we aim to ensure that this lead is sustained well into the future. Cates, Professor University of EP/S005684/1 EPSRC Centre for Doctoral Training in Informed Design of Soft Materials M Cambridge Many products are based on the design and manufacture of complex soft materials. Examples are emulsions (mayonnaise), dense suspensions (toothpaste), polymers (engine oil), and liquid crystals (the slime you get when a bar of soap is left in a pool of water). Each of these has higher tech counterparts in drug delivery, advanced ceramics precursors, modern plastics, and display devices for smartphones and TVs. The innovation process for these materials has to be speeded up. Modern soft materials for new applications such as energy storage (electric vehicle batteries), fully biodegradable plastics, and many other areas need to be created in the research lab, perfected by industry, and emerge onto the market in a few years rather than a few decades. This requires not only an improved understanding of the microscopic mechanisms by which soft material products work, but also a modern replacement for the traditional formulation approach, which was largely based on old- fashioned empiricism or "trial and error". Modern empiricism is now being developed across many areas of science (as well as by google, facebook and others to learn your marketing preferences from your behaviour and that of our friends). It involves spotting hidden patterns in the data, which can allowing reliable prediction of what works, even without a microscopic understanding of why. While this 'chemical informatics' approach is established for individual molecules such as drugs, it remains in its infancy for materials science and has barely started at all for complex soft materials. To be successful there, it will need to be properly integrated with mechanistic information driven by basic science, to avoid data-driven predictions that fail to respect the known laws of physics or chemistry. Because such laws create very complicated constraints on soft materials design, we need train a new generation of scientists able to bring together these two very different types of information to reach new conclusions. Such scientists will also need to be skilled in drawing on a third source of information: how nature has evolved its own complex soft materials such as biological tissues, and how these materials work with each other to create highly functional components -- such as a kidney (say) which filters unwanted chemicals out of our bodies with amazing efficiency. Sometimes nature's approach cannot be bettered, in which case we should try to copy it, but in other instances it can be improved. Either way, we need to know how to work with nature rather than against it, and build that knowledge into the design of new soft materials from day one. The current problem of non-biodegradable plastics accumulating in the ocean is just one example of how badly things can go wrong. Gaunt, Professor University of EPSRC Centre for Doctoral Training in Automated Chemical Synthesis Enabled EP/S005722/1 M Cambridge by Digital Molecular Technologies Efficient synthesis remains a bottleneck in the drug discovery process. Access to novel biologically active molecules to treat diseases continues to be a major bottleneck in the pharmaceutical industry, costing many lives and many £millions per year in healthcare investment and loss in productivity. In 2016, the Pharmaceutical Industry's estimated annual global spend on research and development (R&D) was over $157 billion. At a national level, the pharmaceutical sector accounted for almost half of the UK's 2016 £16.5bn R&D expenditure, with £700 million invested in pre-clinical small molecule synthesis, and 995 pharmaceutical related enterprises (big pharma, SMEs, biotech & CROs) employing around 23,000 personnel in UK R&D. The impact of this sector and its output on the nation's productivity is indisputable and worthy of investment in new Page 2 of 183 approaches and technologies to fuel further innovation and development. With an increasing focus on precision medicine and genetic understanding of disease there will be to a dramatic increase in the number of potent and highly selective molecular targets; identifying genetically informed targets could double success rates in clinical development (Nat. Gen. 2015, 47, 856). However, despite tremendous advances in chemical research, we still cannot prepare all the molecules of potential interest for drug development due to cost constraints and tight commercial timelines. By way of example, Merck quote that 55% of the time, a benchmarked catalytic reaction fails to deliver the desired product; this statistic will be representative across pharma and will apply to many comparable processes. If more than half of the cornerstone reactions we attempt fail, then we face considerable challenges that will demand a radical and innovative a step change in synthesis. Such a paradigm shift in synthesis logic will need to be driven by a new generation of highly skilled academic and industry researchers who can combine innovative chemical syntis and technological advances with fluency in the current revolution in data-driven science, machine learning methods and artificial intelligence. Synthetic chemists with such a set of skills do not exist anywhere in the world, yet the worldwide demand for individuals with the ability to work across these disciplines is increasing rapidly, and will be uniquely addressed by this proposed CDT. By training the next generation of researchers to tackle problems in synthetic chemistry using digital molecular technologies, we will create a unique, highly skilled research workforce that will address these challenges and place UK academic and industrial sectors at the frontier of molecule building science. The aspiration of next-generation chemical synthesis should be to prepare any molecule of interest without being limited by the synthetic methodologies and preparation technologies we have relied on to date. Synthetic chemists with the necessary set of such skills and exposure to the new technologies, required to innovate beyond the current
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