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.

Smart environments are designed to react intelligently to the needs of those who visit, live and work in them. For example, the lights can come on when it gets dark in a living room or a video exhibit can play in the correct language when a museum visitor approaches it. However, we lack intuitive ways for users without technical backgrounds to understand and reconfigure the behaviours of such environments, and there is considerable public mistrust of automated environments. Whilst there are tools that let users view and change the rules defining smart environment behaviours without having programming knowledge, they have not seen wide uptake beyond technology enthusiasts. One drawback of existing tools is that they pull attention away from the environment in question, requiring users to translate from real world objects to abstract screen-based representations of them. New programming tools that allow users to harness their understandings of and references to objects in the real world could greatly increase trust and uptake of smart environments. This research will investigate how users understand and describe smart environment behaviours whilst in situ, and use the findings to develop more intuitive programming tools. For example, a tool could let someone simply say that they want a lamp to come on when it gets dark, and point at it to identify it. Speech interfaces are now widely used in intelligent personal assistants, but the functionality is largely limited to issuing immediate commands or setting simple reminders. In reality, there are many challenges with using speech interfaces for programming tasks, and idealised interactions such as the lamp example are not at all simple, in reality. In many cases, research used to design programming interfaces for everyday users is carried out in research labs rather than in the real home or workplace settings, and the people invited to take part in design and evaluation studies are often university students or staff, or people with an existing interest or background in technology. These interfaces often fall down once taken away from the small set of toy usage scenarios in which they have been designed and tested and given to everyday users. This research investigates the challenges with using speech for programming, and evaluates ways to mitigate these challenges, including conversational prompts, use of gesture and proximity data to avoid ambiguity, and providing default behaviours that can be customised. In this project, we focus primarily on smart home scenarios, and we will carry out our studies in real domestic settings. Speech interfaces are increasingly being used in these scenarios, but there is no support for querying, debugging and alternating the behaviours through speech. We will recruit participants with no programming background, including older and disabled users, who are often highlighted as people who could benefit from smart home technology, but rarely included in studies of this sort. We will carry out interviews in people's homes to understand how they naturally describe rules for smart environments, taking into account speech, gesture and location. We will look for any errors or unclear elements in the rules they describe, and investigate how far prompts from researchers can help them to be able to express the rules clearly. We will also explore how far participants can customise default behaviours presented to them. This data will be used to allow us to create a conversational interface that harnesses the approaches that worked with human prompts, and test it in real world settings. Some elements of the system will be controlled by a human researcher, but the system will simulate the experience of interacting with an intelligent conversational interface. This will allow us to identify fruitful areas to pursue in developing fully functional conversational programming tools, which may also be useful in museums, education, agriculture and robotics.

Renewable and low carbon energy sources need to be more competitive if the world is to meet the carbon emissions targets agreed in COP21. CAMREG brings together cutting edge materials researchers who will work across discipline boundaries to increase renewable energy technology durability, reliability, utility, performance and energy yield. The aim of the Centre is to combine activity, know-how and facilities from a wide range of existing fundamental and applied materials science capacity to address the known and emerging challenges in renewable energy generation, including on- and off-shore wind, wave, tidal, conventional and next-generation solar photovoltaics and energy storage. CAMREG will support and hasten the establishment or expansion of viable and sustainable renewable energy industries in the UK. The proposed centre offers a wide breadth and considerable depth of materials research capability and capacity in many areas of renewable energy and is aimed at reducing the overall levelised cost of energy to the consumer. The centre addresses 4 of the suggested areas in the Call in the following 3 themes: multifunctional materials for energy applications; materials for energy conversion & storage and smart materials for energy applications. Research areas include: efficient materials for PV and energy storage; materials for increased power density in electrical generators; improved design and testing of composite blades for wind and tidal turbines; smart materials and optical coatings that detect early damage in wind blades; smart coatings to minimise erosion and corrosion on blades and offshore support towers; lighter-weight design of structural steels; large-scale structural testing of components; better materials fatigue and failure management; lower-maintenance materials with improved resistance to wear and corrosion; superconducting materials to transfer power over long distances with less losses; high temperature ceramics and molten salt for energy storage; electrically responsive artificial muscles that can morph the shapes of wind turbine blades to ensure better energy yields, materials for increased conversion efficiency and better mooring for wave and tidal devices. CAMREG is a partnership of 3 research-intensive universities, Edinburgh, Cranfield and Strathclyde, which would gather and network the interests, capacity and networks of many of the RCUK investments in energy research and training, and accruing over 200 industry connections: through 3 SuperGen Hubs, Marine UKCMER, Wind and Power Networks; 4 EPSRC Centres for Doctoral Training - Wind Energy Systems, Wind & Marine Energy Systems, Offshore Renewable Energy Marine Structures and Integrative Sensing and Measurement; the EPSRC Industrial Doctorate Centre in Offshore Renewable Energy and the DECC SLIC (Offshore Wind Structural Lifecycle) Joint Industry Project - the largest industry-funded offshore renewables related materials and structures research project worldwide, involving Certification Authorities (DNV-GL and LR) and 10 of Europe's largest energy utility companies. The Centre will also respond to the needs and experience of device developers, project planners, legislators and consenting bodies, and academic partners will continue to work closely with key UK policy stakeholders. CAMREG underpins the efforts at existing recognised centres of renewable energy and materials science research, and encourages networking with new research groups working in complementary areas and linking centres into a coordinated national network. Expected national impacts include: environmental benefits, through increasing the potential to displace fossil fuels; economic benefit through the expansion of employment and human capacity transfer from the existing offshore energy industries; increased diversity, security and resilience of electricity supply through reduction in dependence upon imported fuel and as indigenous coal oil and gas production declines.

Recent satellite measurements of the Earth's polar ice sheets highlight that changes in ice extent and thickness are occurring at rates far higher than expected. The challenge for researchers is to place these observations into a longer-term context and produce computer models ('ice sheet forecasts') that reliably predict the fate of ice sheets over this century and beyond. Although remote from habitation, the polar ice sheets influence global sea level. Retreat by increased melting and iceberg calving produces higher sea levels and concerns exist that sea level may rise by metres displacing many millions of people, and their livelihoods, from their coastal homes. At this point in time, it is not possible to study the full life cycle of the present Antarctic or Greenland ice sheets as they are still evolving and undergoing large-scale changes. Instead, we will use an ice sheet that has now fully retreated; the ice sheet that covered most of Britain, Ireland and the North Sea during the last ice age. The last British-Irish ice sheet covered up to 1,000,000 km2 at its maximum size, around 25,000 yrs ago, and was relatively small by global standards. However, its character, setting and behaviour have striking parallels with both the modern West Antarctic and Greenland Ice Sheets. Large parts of the British-Irish Ice Sheet were marine-influenced just like in west Antarctica today; and numerous fast-flowing ice streams carried much of its mass, just like in the Greenland Ice Sheet today. All three are or were highly dynamic, in climatically sensitive regions, with marine sectors, ocean-terminating margins and land-based glaciers. All these common factors make the British-Irish Ice Sheet a powerful analogue for understanding ice sheet dynamics on a range of timescales, operating now and in the future. Recent work by members of this consortium has revealed the pattern of ice sheet retreat that once covered the British Isles, as recorded by end moraines and other glacial landforms. Other work by members of this consortium has used sophisticated computer models to simulate the ice sheet's response to climate change at the end of the last Ice Age. However, these models can only be as good as the geological data on which they are based, and the pattern is poorly constrained in time. We need to know more about the style, rate and timing of ice sheet decay in response to past climate change. Such knowledge allows us to further refine computer modelling so that better predictions can be made. The main focus of the project therefore, is to collect sediments and rocks deposited by the last ice sheet that covered the British Isles, and use these, along with organic remains, to date (e.g. by radiocarbon analyses) the retreat of the ice sheet margins. The project will use over 200 carefully chosen sites, dating some 800 samples in order to achieve this. Offshore, samples will be extracted using coring devices lowered from a research ship to the seabed, and onshore by manual sampling and by use of small drilling rigs. Once the samples are dated and added to the pattern information provided by the landforms, maps of the shrinking ice sheet will be produced. These will provide crucial information on the timing and rates of change across the whole ice sheet. The British-Irish Ice Sheet will become the best constrained anywhere in the world and be the benchmark against which ice sheet models are improved and tested in the future. Knowledge on the character and age of the seafloor sediments surrounding the British Isles is also useful for many industrial, archaeological and heritage applications. Accordingly, the project is closely linked to partners interested for example in locating offshore windfarms, electricity cables between Britain and Ireland, and heritage bodies aiming to preserve offshore archaeological remains.

The latest report from the Intergovernmental Panel on Climate Change in 2018 highlighted the need for urgent, transformative change, on an unprecedented scale, if global warming is to be restricted to 1.5C. The challenge of reaching an 80% reduction in emissions by 2050 represents a huge technological, engineering, policy and societal challenge for the next 30 years. This is a huge challenge for the transport sector, which accounts for over a quarter of UK domestic greenhouse gas emissions and has a flat emissions profile over recent years. The DecarboN8 project will develop a new network of researchers, working closely with industry and government, capable of designing solutions which can be deployed rapidly and at scale. It will develop answers to questions such as: 1) How can different places be rapidly switched to electromobility for personal travel? How do decisions on the private fleet interact with the quite different decarbonisation strategies for heavy vehicles? This requires integrating understanding of the changing carbon impacts of these options with knowledge on how energy systems work and are regulated with the operational realities of transport systems and their regulatory environment; and 2) What is the right balance between infrastructure expansion, intelligent system management and demand management? Will the embodied carbon emissions of major new infrastructure offset gains from improved flows and could these be delivered in other ways through technology? If so, how quickly could this happen, what are the societal implications and how will this impact on the resilience of our systems? The answer to these questions is unlikely to the same everywhere in the UK but little attention is paid to where the answers might be different and why. Coupled with boundaries between local government areas, transport network providers (road and rail in particular) and service operators there is potential for a lack of joined up approaches and stranded investments in ineffective technologies. The DecarboN8 network is led by the eight most research intensive Universities across the North of England (Durham, Lancaster, Leeds, Liverpool, Manchester, Newcastle, Sheffield and York) who will work with local, regional and national stakeholders to create an integrated test and research environment across the North in which national and international researchers can study the decarbonisation challenge at these different scales. The DecarboN8 network is organised across four integrated research themes (carbon pathways, social acceptance and societal readiness, future transport fuels and fuelling, digitisation, demand and infrastructure). These themes form the structure for a series of twelve research workshops which will bring new research interests together to better understand the specific challenges of the transport sector and then to work together on integrating solutions. The approach will incorporate throughout an emphasis on working with real world problems in 'places' to develop knowledge which is situated in a range of contexts. £400k of research funding will be available for the development of new collaborations, particularly for early career researchers. We will distribute this in a fair, open and transparent manner to promote excellent research. The network will help develop a more integrated environment for the development, testing and rapid deployment of solutions through activities including identifying and classifying data sources, holding innovation translation events, policy discussion forums and major events to highlight the opportunities and innovations. The research will involve industry and government stakeholders and citizens throughout to ensure the research outcomes meet the ambitions of the network of accelerating the rapid decarbonisation of transport.