Carbonate platforms form some of the most economically important hydrocarbon reservoirs. Whilst carbonate production rates and the composition of platform sedimentary facies are primary controlled by marine environmental parameters, larger-scale patterns of platform development (and internal platform architecture) are most strongly influenced by the interplay between eustatic sea-level change and basinal subsidence or uplift. Particularly important to reservoir potential is the subsidence regime and drowning history experienced by a platform. Platform drowning occurs when rates of relative sea-level rise exceed rates of vertical sediment accumulation. This acts as a major control on reservoir potential by influencing the location and extent of diagenetic seals and permeability barriers within and on top of platform surfaces. These seals and permeability barriers are produced by successive cycles of diagenetic and taphonomic alteration as platforms are progressively drowned and/or subject to subaerial exposure- typically they are the result of sequential over-printing of previous phases of diagenetic alteration. Unravelling the diagenetic history of platform drowning surfaces and using this information to interpret platform drowning events in the rock record is particularly difficult due to the paucity of detailed diagenetic studies from actively subsiding platform margins - this is largely a function of the inaccessibility of 'recent' platform margin deposits. The issue of drowning surface development and drowning histories has direct relevance to several carbonate reservoir interests operated by the CASE partner (BG Group), most especially at the present time within the Karachaganak Field in Kazakhstan, as well as to many other carbonate platform reservoir deposits. The Karachaganak Field is a 'supergiant' gas condensate field in NW Kazakhstan with estimated reserves of 12.3 billion barrels of oil and 57 trillion cubic feet of gas. Drowning surfaces are critically important because they act as vertical barriers to fluid flow, although recent development wells have indicated that these barriers are not effective over the entire platform. Understanding spatial variations in these drowning surfaces is thus a key subsurface challenge for the operators, since it crucially impacts the design of a gas injection system designed to maintain reservoir pressure support. The paucity of appropriate analogue work on drowning surface diagenesis and likely spatial extent thus severely restricts the operator's ability to interpret drowning histories or to model vertical permeability patterns. The aim of this project is to utilise the unique sample collections recovered from successive drowned platforms around Hawaii and the Huon Gulf to examine the effects of progressive drowning on platform diagenesis. The Huon Gulf sequence comprises nine platforms, preserved across water depths of 239 m to 2,393 m and spans an age range from ~60 to ~450 ka. Around Hawaii twelve platforms have been mapped and sampled. These occur across depths from ~125 m to 1,400 m and span an age of ~15 to 500 ka. The student will utilise this unique dataset to examine temporal variations in the evolution of diagenetic and taphonomic fabrics associated with progressive platform drowning, focusing specifically on the evolution and successive over-printing effects of marine cementation and taphonomic alteration (bioerosion, encrustation, dissolution). The student will also undertake fieldwork in Kazakhstan (working with the CASE supervisor) to examine core, wireline log and other data related to the drowning surfaces in the Karachaganak Field in order to examine the petrography and character of platform drowning surfaces in one of the BG operated fields. Samples from these core horizons will be analysed in the context of the findings from Hawaii and the Huon Gulf in order to improve interpretation of the drowning history based on the previously developed criteria.

Carbonate platforms form some of the most economically important hydrocarbon reservoirs. Whilst carbonate production rates and the composition of platform sedimentary facies are primary controlled by marine environmental parameters, larger-scale patterns of platform development (and internal platform architecture) are most strongly influenced by the interplay between eustatic sea-level change and basinal subsidence or uplift. Particularly important to reservoir potential is the subsidence regime and drowning history experienced by a platform. Platform drowning occurs when rates of relative sea-level rise exceed rates of vertical sediment accumulation. This acts as a major control on reservoir potential by influencing the location and extent of diagenetic seals and permeability barriers within and on top of platform surfaces. These seals and permeability barriers are produced by successive cycles of diagenetic and taphonomic alteration as platforms are progressively drowned and/or subject to subaerial exposure- typically they are the result of sequential over-printing of previous phases of diagenetic alteration. Unravelling the diagenetic history of platform drowning surfaces and using this information to interpret platform drowning events in the rock record is particularly difficult due to the paucity of detailed diagenetic studies from actively subsiding platform margins - this is largely a function of the inaccessibility of 'recent' platform margin deposits. The issue of drowning surface development and drowning histories has direct relevance to several carbonate reservoir interests operated by the CASE partner (BG Group), most especially at the present time within the Karachaganak Field in Kazakhstan, as well as to many other carbonate platform reservoir deposits. The Karachaganak Field is a 'supergiant' gas condensate field in NW Kazakhstan with estimated reserves of 12.3 billion barrels of oil and 57 trillion cubic feet of gas. Drowning surfaces are critically important because they act as vertical barriers to fluid flow, although recent development wells have indicated that these barriers are not effective over the entire platform. Understanding spatial variations in these drowning surfaces is thus a key subsurface challenge for the operators, since it crucially impacts the design of a gas injection system designed to maintain reservoir pressure support. The paucity of appropriate analogue work on drowning surface diagenesis and likely spatial extent thus severely restricts the operator's ability to interpret drowning histories or to model vertical permeability patterns. The aim of this project is to utilise the unique sample collections recovered from successive drowned platforms around Hawaii and the Huon Gulf to examine the effects of progressive drowning on platform diagenesis. The Huon Gulf sequence comprises nine platforms, preserved across water depths of 239 m to 2,393 m and spans an age range from ~60 to ~450 ka. Around Hawaii twelve platforms have been mapped and sampled. These occur across depths from ~125 m to 1,400 m and span an age of ~15 to 500 ka. The student will utilise this unique dataset to examine temporal variations in the evolution of diagenetic and taphonomic fabrics associated with progressive platform drowning, focusing specifically on the evolution and successive over-printing effects of marine cementation and taphonomic alteration (bioerosion, encrustation, dissolution). The student will also undertake fieldwork in Kazakhstan (working with the CASE supervisor) to examine core, wireline log and other data related to the drowning surfaces in the Karachaganak Field in order to examine the petrography and character of platform drowning surfaces in one of the BG operated fields. Samples from these core horizons will be analysed in the context of the findings from Hawaii and the Huon Gulf in order to improve interpretation of the drowning history based on the previously developed criteria.

Long-term energy consumption reduction can be achieved more readily through sensible cooperation between end users and technological advancements. Monitoring energy use within buildings requires clear and reliable methods with outputs that are meaningful and helpful. End users play a pivotal role in this as energy use revolves around their presence and comfort. Hence, with changing lifestyles and working patterns, energy consumption reduction can be aided by new approaches in digital innovation. Energy metering schemes are now popular and provide data on energy use and cost, but communicatively are a one-way street. Hence, this information is only beneficial if users continually make changes to utility use within their home. However, behavioural changes inducing energy reduction fade relatively quickly and users feel less empowered. Last year, residential sector emissions rose by 13.4% despite metering being a popular investment. Based on this information, interactive systems can help address this problem. Consumers appreciate that innovative technology can increase their quality of life. However, a lasting bond between the two can only occur when users have confidence in the technology around them. This is more likely to happen when users and technologists work collectively in the system design process. DANCER takes insights from users' behaviour analysis, metering schemes, wireless sensors and embedded software to produce a system that both interactively and automatically manages users' energy consumption within indoor environments. It will tailor users' energy consumption to their habits aiming to reduce energy consumption. To achieve this DANCER adopts a multidisciplinary approach where knowledge from psychology, social and economic research, wireless communication and computer science unite to provide a viable solution that is beneficial to all the stakeholders on the energy supply-consumer chain. To the above aim, users' energy consumption habits will be collected and studied to inform both the design of the energy control system as well as the user interface in the DANCER system. Baseline information will be collected from samples of end users. This will be combined with insights from the relevant emerging literature. Moreover, an iterative participatory design approach will be used to explore how users feel about the digital technologies to be employed in this project and how they imagine these technologies can assist them in reducing their energy consumption and carbon footprint. Increasingly mature versions of the DANCER system will then be pre-tested through a series of pilot studies with volunteers so that users' queries about the sensors, networks and control policies being used to monitor and interactively manage their energy use can be further examined. Finally, the mature DANCER system will be tested in a control trial experiment where samples of households will be either provided with DANCER or allocated to appropriate control conditions. The trial will enable the analysis of the effect of the system on users' energy related behaviours, energy and carbon emissions. The DANCER system will act as follows. Wireless sensor networks will employ novel sensing and communication mechanisms which will monitor users' movements and the energy use of a range of appliances. These data, together with the information either collected directly from end users via their smart phone application (e.g. indications to reduce energy use by 20%) or inferred indirectly from user habits, will be fed into a decision making agent that will decide when to switch on/off certain appliances and for how long. The above information collected by the agent, on a per-dwelling basis, will be sent to a centralised remote database. As a result, a global view of the energy consumption and user habits can be derived. In return, this information can be used to guide stakeholders to more effective and efficient way of supplying and consuming energy.

This century is set to be the century of the city. Ever-increasing urbanisation is proceeding against a backdrop of advances in digital technologies and data availability and analysis, which are having profound effects on the ways that the future of cities is unfolding. Emerging from this intersection of urban growth and 'big data' is the discipline of urban science which can assist governments, industry and citizens to move beyond imperfect understanding and use data to undertake tasks such as optimising operations (e.g. service delivery, traffic flow), monitoring the condition of infrastructure (e.g. bridge conditions, water leaks), planning new, more efficient, infrastructure (e.g. public transport, utilities provision), responding to abnormal conditions (e.g. hazard detection, emergency management), developing new and effective policies (e.g. road pricing, energy efficient buildings), enhancing economic performance and, informing and communicating with citizens to improve quality of life. This Centre for Doctoral Training (CDT) is designed to play a leading role in the emergence and development of urban science. It will establish urban science as a field of study and focus of scientific inquiry. This new field needs trained cross-disciplinary researchers, who have the skills to integrate diverse branches of knowledge to address a range of important current and future policy drivers. It will build capacity within the UK HE sector to deliver novel solutions in the urban science domain, both nationally and internationally. Importantly, it will do so in an interdisciplinary environment, e.g. by exploiting synergies between computer science, engineering, mathematics and social science. Solutions to urban issues require a tri-partied relationship between academia, public bodies and the private sector. This CDT will work alongside government agencies and industry partners in the UK and abroad. The importance of urban science and appropriate cross-disciplinary research is central to our CDT approach. The potential benefits and impact are listed by the leader of Birmingham City Council as including "mak[ing] a real difference to tens of thousands of Birmingham residents", "saving £Ms in operating costs", and "deliver[ing] a legacy of change through the training of individuals who have real expertise in their area". The deputy mayor of New York states that the centre can "develop scientific solutions that will have direct impact on billions of the world's population." This CDT provides a UK training environment that is part of a wider international programme, which offers training alongside international city experts, and benefits from the support of leading industry practitioners. No one in the world is tackling urban challenges at this scale. By leading the research agenda on the science of cities, educating the next generation of experts in how to apply that research, bringing innovative ideas to a world market, and creating new, fast-growing industry solutions and the many jobs that go with them, this UK-led CDT will be at the centre of the global stage in this field. The CDT will adopt a 1+3 (MSc+PhD) training model that is high-quality and rigourous, to produce multiple cohorts of successful, highly-employable graduates. It promotes an international student experience; students will work alongside a larger student cohort from NYU, CUNY, Carnegie Mellon University, University of Toronto and IIT Mumbai; it allows our students unprecedented access, in the UK and overseas, to existing city operations, to utilize existing and newly emerging data streams, and to explore and deploy novel urban sensors; it enables students to work alongside industry luminaries, leaders in public service and citizens, to understand, measure and improve urban systems; and it provides value for money to the UK through 50+ PhDs who will receive discipline-defining training from world-class institutions.

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.