Wearable neurotechnology utilization is expected to increase dramatically in the coming years, with applications in enabling movement-independent control and communication, rehabilitation, treating disease and improving health, recreation and sport among others. There are multiple driving forces:- continued advances in underlying science and technology; increasing demand for solutions to repair the nervous system; increase in the ageing population worldwide producing a need for solutions to age-related, neurodegenerative disorders, and "assistive" brain-computer interface (BCI) technologies; and commercial demand for nonmedical BCIs. There is a significant opportunity for the UK to lead in the development of AI-enabled neurotechnology R&D. There are a number of key challenges to be addressed, mainly associated with the complexity of signals measured from the brain. AI has the potential to revolutionise the neurotechnology industry and neurotechnology presents an excellent challenge for AI. This fellowship will build on the award-winning AI and neurotechnology research of the fellow and offer real potential for impact through established clinical partnerships and in the neurotechnology industry. The objective of this project is to build on award-winning AI and neurotechnology R&D to address key shortcomings of neurotechnology that limit its widespread use and adoption using a range of key neural network technologies in a state-of-the-art framework for processing neural signals developed by the proposed fellow. The AI technologies developed for neurotechnology will be applied across sectors to demonstrate translational AI through engagement with at least 10 companies across at least 5 sectors during the fellowship, to demonstrate societal and economic benefit and interdisciplinary and translational AI skills development. The project has multiple industry, clinical and academic partners and is expected to produce world-leading AI technologies and propel the fellow to world-leading status in developing AI for neurotechnology which will impact widely. A major focus of the project is ensuring the expectations of the fellow role are met. This includes:- -Ensuring the processes and resources are in place to build a world-leading profile by the end of the fellowship; -Focusing on planning research of the team as new results emerge and hypothesis are tested, to refine and develop a high-quality programme of ambitious, novel and creative research, in AI-enabled Neurotechnology. Specific focus will be ensuring meticulous planning, execution and follow-up to produce world-leading results; -Continuing to perform my leadership role as director of the ISRC and leader of the data analytics theme, expanding the team and actively seek to develop into a position of higher leadership of the research agenda at Ulster, and in the national and international research community; -Focusing on strengthening relationships and collaborations with colleagues in industry and academia, and maximising the potential for flexible career paths for researchers within the team -Acting as an ambassador and advocate for AI, science and ED&I including by continuing to actively provide opinions and engaging with questions around AI and ethics, and responsible research and innovation (RRI). A focus will be embedding this throughout the activities of the fellowship but across the region and internationally; -Seeking to engage with and influence the strategic direction of the UK AI research and innovation landscape through engagement with their peers, policymakers, and other stakeholders including the public through. -Ensuring that the fundamental research is developed to have a high likelihood of impact on UK society/economy through trials across a range of patient groups to develop the evidence base and transfer of intellectual property to products, in particular through NeuroCONCISE Ltd, a main project partner.

The Peta-5 proposal from the University of Cambridge brings together 15 world-leading HPC system and application experts from 10 different institutions to lead the creation of a breakthrough HPC and data analytics capability that will deliver significant National impact to the UK research, industry and health sectors. Peta-5 aims to make a significant contribution towards the establishment and sustainability of a new EPSRC Tier 2 HPC network. The Cambridge Tier 2 Centre working in collaboration with other Tier 1, Tier 2 and Tier 3 stakeholders aims to form a coherent, coordinated and productive National e-Infrastructure (Ne-I) ecosystem. This greatly strengthened computational research support capability will enable a significant increase in computational and data centric research outputs, driving growth in both academic research discovery and the wider UK knowledge economy. The Peta-5 system will be one of the largest heterogeneous data intensive HPC systems available to EPSRC research in the UK. In order to create the critical mass in terms of system capability and capacity needed to make an impact at National level Cambridge have pooled funding and equipment resources from the University, STFC DiRAC and this EPSRC Tier 2 proposal to create a total capital equipment value of £11.5M; the request to EPSRC is £5M. The University will guarantee to cover all operational costs of the system for 4 years from the service start date, with the option to run for a fifth year to be discussed. Cambridge will ensure that 80% of the EPSRC funded element of Peta-5 is deployed on EPSRC research projects, with 65% of the EPSRC funded element of Peta-5 being made available to any UK EPSRC funded project free of charge by use of a light weight resource allocation committee, 15% going to Cambridge EPSRC research and 20% being sold to UK industry to drive the UK knowledge economy. The Peta-5 system will be the most capable HPC system in operation in the UK when it enters service in May 2017. In total Peta-5 will provide 3 petaflops (PF) of sustained performance derived from 3 heterogeneous compute elements, 1PF Intel X86, 1PF Intel KNL and 1PF NIVIDIA Pascal GPU (Peta-1) connected via a Pb/s HPC fabric (Peta-2) to an extreme I/O solid state storage pool (Peta-3), a petascale data analytics (Machine Learning + Hadoop) pool (Peta-4) and a large 15 PB tiered storage solution (Peta-5), all under a single execution environment. This creates a new HPC capability in the UK specifically designed to meet the requirements of both affordable petascale simulation and data intensive workloads combined with complex data analytics. It is the combination of these features which unlocks a new generation of computational science research. The core science justification for the Peta-5 service is based on three broad science themes: Materials Science and Computational Chemistry; Computational Engineering and Smart Cities; Health Informatics. These themes were chosen as they represent significant EPSRC research areas, which demonstrate large benefit from the data intensive HPC capability of Peta-5. The service will clearly be valuable for many other areas of heterogeneous computing and Data Intensive science. Hence a fourth horizontal thematic of "Heterogeneous - Data Intensive Science" is included. Initial theme allocation in the RAC will be: Materials 30%, Engineering 30%, Health, 20%, Heterogeneous - Data Intensive 20%. The Peta-5 facility will drive research discovery and impact at national level, creating the largest and most cost effective petascale HPC resource in the UK, bringing petascale simulation within the reach of a wide range of research projects and UK companies. Also Peta-5 is the first UK HPC system specifically designed for large scale machine learning and data analytics, combining the areas of HPC and Big Data, promising to unlock both knowledge and economic benefit from the Big Data revolution.

Imaging methods are used to obtain visual representations of objects that are otherwise invisible to the naked eye. The physical principles in which imaging methods are based are common across disciplines and, hence, can be adapted. Here I propose to lead an inter-disciplinary project that will focus on obtaining images of medical and geophysical targets that are traditionally difficult to image with ultrasound or seismic waves, such as the brain. Rapid brain imaging is central to the diagnosis and treatment of stroke and other acute neurological conditions, but existing methods for imaging the brain (mainly X-rays and magnetic resonance imaging) require large, immobile, high-power instruments that are near-impossible to deploy outside specialised environments. I will create a device that can be applied to any patient, at any time and in any place by exploiting advances that have already revolutionised imaging in geophysics and using ultrasound waves transmitted across the head. In particular, I will adapt an imaging algorithm known as full-waveform inversion to transform the recorded ultrasound data into the first highly detailed image of an adult brain with ultrasound, and with a much higher resolution than those obtained with conventional ultrasound. To achieve this goal, I will design a safe and suitable device for its application to healthy volunteers, and I will use the recorded data and full-waveform inversion conveniently adapted. This will require solving several technical aspects, such as accounting for involuntary movement due to breathing, obtaining the characteristics of the skull from the data and accelerating the computations on graphics processing units. The success of this project would represent a major breakthrough in brain imaging and would be particularly relevant to improve the survival rate and wellbeing of patients with acute stroke, which is the second-largest cause of death and acquired adult disability. Then, I will study the capability of ultrasound full-waveform inversion for breast cancer detection, in particular for patients with dense breasts in which traditional mammography fails, and for bone imaging - in particular for detecting osteoporosis and fractures. To achieve these goals, I will develop and validate in the laboratory new full-waveform inversion algorithms to recover multiple characteristics of biological tissues and I will use low-frequency ultrasound that easily penetrates bone. Next, I will investigate the potential of full-waveform inversion of ultrahigh-frequency seismic data, a particular type of seismic waves that travel small distances but can interact with small objects, in order to characterise the first 100 meters of the subsurface in offshore wind farms. This new approach will be particularly useful to characterise vast areas of the subsurface and locate adequate regions for the installation of wind turbines to reduce maintenance costs. Finally, I will evaluate different strategies to obtain subsurface images over time with full-waveform inversion of seismic data at carbon dioxide storage sites, which play a crucial role in reducing the carbon footprint. This will help engineers better understand how carbon dioxide reservoirs evolve and how to make them safer and more efficient.

Powered by data, Industrial Digital Technologies (IDTs) such as artificial intelligence and autonomous robots, can be used to improve all aspects of manufacturing and supply of products along supply chains to the customer. Many companies are embracing these technologies but uptake within the pharmaceutical sector has not been as rapid. The Medicines Made Smarter Data Centre (MMSDC) looks to address the key challenges which are slowing digitalisation, and adoption of IDTs that can transform processes to deliver medicines tailored to patient needs. Work will be carried out across five integrated platforms designed by academic and industrial researcher teams. These are: 1) The Data Platform, 2) Autonomous MicroScale Manufacturing Platform, 3) Digital Quality Control Platform, 4) Adaptive Digital Supply Platform, and 5) The MMSDC Network & Skills Platform. Platform 1 addresses one of the sector's core digitalisation challenges - a lack of large data sets and ways to access such data. The MMSDC data platform will store and analyse data from across the MMSDC project, making it accessible, searchable and reusable for the medicines manufacturing community. New approaches for ensuring consistently high-quality data, such as good practice guides and standards, will be developed alongside data science activities which will identify what the most important data are and how best to use them with IDTs in practice. Platform 2 will accelerate development of medicine products and manufacturing processes by creating agile, small-scale production facilities that rapidly generate large data sets and drive research. Robotic technologies will be assembled to create a unique small-scale medicine manufacturing and testing system to select drug formulations and processes to produce stable products with the desired in-vitro performance. Integrating several IDTs will accelerate drug product manufacture, significantly reducing experiments and dramatically reducing development time, raw materials and associated costs. Platform 3 focusses on the digitalisation of Quality Control (QC) aspects of medicines development which is important for ensuring a medicine's compliance with regulatory standards and patient safety requirements. Currently, QC checks are carried out after a process has been completed possibly spotting problems after they have occurred. This approach is inefficient, fragmented, costly (>20% of total production costs) and time consuming. The digital QC platform will research how to transform QC by utilising rich data from IDTs to confirm in real time product and process compliance. Platform 4 will generate new understanding on future supply chain needs of medicines to support adoption of adaptive digital supply chains for patient-centric supply. IDTs make smaller scale, autonomous factory concepts viable that support more flexible and distributed manufacture and supply. Supply flexibility and agility extends to scale, product variety, and shorter lead-times (from months to days) offering a responsive patient-centric or rapid replenishment operating model. Finally, technology developments closer to the patient, such as diagnostics provide visibility on patient specific needs. Platform 5 will establish the MMSDC Network & Skills Platform. This Network will lead engagement and collaboration across key stakeholder groups involved in medicines manufacturing and investments. The Network brings together the IDT-using community and other relevant academic and industrial groups to share developments across pharmaceuticals and broader digital manufacturing sectors ensuring cross-sector diffusion of MMSDC research. Existing strategic networks will support MMSDC and act as gateways for IDT dissemination and uptake. The lack of appropriate skills in the workforce has been highlighted as a key barrier to IDT adoption. An MMSDC priority is to identify skills needs and with partners develop and deliver training to over 100 users

Lattice Field Theory (LFT) provides the tools to study the fundamental forces of nature using numerical simulations. The traditional realm of application of LFT has been Quantum Chromodynamics (QCD), the theory describing the strong nuclear force within the Standard Model (SM) of particle physics. These calculations now include electromagnetic effects and achieve sub percent accuracy. Other applications span a wide range of topics, from theories beyond the Standard Model, to low-dimensional strongly coupled fermionic models, to new cosmological paradigms. At the core of this scientific endeavour lies the ability to perform sophisticated and demanding numerical simulations. The Exascale era of High Performance Computing therefore looks like a time of great opportunities. The UK LFT community has been at the forefront of the field for more than three decades and has developed a broad portfolio of research areas, with synergetic connections to High-Performance Computing, leading to significant progress in algorithms and code performance. Highlights of successes include: influencing the design of new hardware (Blue Gene systems); developing algorithms (Hybrid Monte Carlo) that are used widely by many other communities; maximising the benefits from new technologies (lattice QCD practitioners were amongst the first users of new platforms, including GPUs for scientific computing); applying LFT techniques to new problems in Artificial Intelligence. The research programme in LFT, and its impact, can be expanded in a transformative way with the advent of pre-Exascale and Exascale systems, but only if key challenges are addressed. As the number of floating point operations per second increases, the communications between computing nodes are lagging behind, and this imbalance will severely affect future LFT simulations across the board. These challenges are common to all LFT codebases, and more generally to other communities that are large users of HPC resources. The bottlenecks on new architectures need to be carefully identified, and software that minimises the communications must be designed in order to make the best usage of forthcoming large computers. As we are entering an era of heterogeneous architectures, the design of new software must clearly isolate the algorithmic progress from the details of the implementation on disparate hardware, so that our software can be deployed efficiently on forthcoming machines with limited effort. The goal of the EXA-LAT project is to develop a common set of best practices, KPIs and figures of merit that can be used by the whole LFT community in the near future and will inform the design and procurement of future systems. Besides the participation of the LFT community, numerous vendors and computing centres have joined the project, together with scholars from 'neighbouring' disciplines. Thereby we aim to create a national and international focal point that will foster the activity of scholars, industrial partners and Research Sotfware Engineers (RSEs). This synergetic environment will host training events for academics, RSEs and students, which will contribute to the creation of a skilled work force immersed in a network that comprises the leading vendors in the subject. EXA-LAT will set the foundations for a long-term effort by the LFT community to fully benefit of Exascale facilities and transfer some of the skills that characterise our scientific work to a wider group of users across disciplines.