Reducing carbon emissions and securing energy supplies are crucial international goals to which energy demand reduction must make a major contribution. On a national level, demand reduction, deployment of new and renewable energy technologies, and decarbonisation of the energy supply are essential if the UK is to meet its legally binding carbon reduction targets. As a result, this area is an important theme within the EPSRC's strategic plan, but one that suffers from historical underinvestment and a serious shortage of appropriately skilled researchers. Major energy demand reductions are required within the working lifetime of Doctoral Training Centre (DTC) graduates, i.e. by 2050. Students will thus have to be capable of identifying and undertaking research that will have an impact within their 35 year post-doctoral career. The challenges will be exacerbated as our population ages, as climate change advances and as fuel prices rise: successful demand reduction requires both detailed technical knowledge and multi-disciplinary skills. The DTC will therefore span the interfaces between traditional disciplines to develop a training programme that teaches the context and process-bound problems of technology deployment, along with the communication and leadership skills needed to initiate real change within the tight time scale required. It will be jointly operated by University College London (UCL) and Loughborough University (LU); two world-class centres of energy research. Through the cross-faculty Energy Institute at UCL and Sustainability Research School at LU, over 80 academics have been identified who are able and willing to supervise DTC students. These experts span the full range of necessary disciplines from science and engineering to ergonomics and design, psychology and sociology through to economics and politics. The reputation of the universities will enable them to attract the very best students to this research area.The DTC will begin with a 1 year joint MRes programme followed by a 3 year PhD programme including a placement abroad and the opportunity for each DTC student to employ an undergraduate intern to assist them. Students will be trained in communication methods and alternative forms of public engagement. They will thus understand the energy challenges faced by the UK, appreciate the international energy landscape, develop people-management and communication skills, and so acquire the competence to make a tangible impact. An annual colloquium will be the focal point of the DTC year acting as a show-case and major mechanism for connection to the wider stakeholder community.The DTC will be led by internationally eminent academics (Prof Robert Lowe, Director, and Prof Kevin J Lomas, Deputy Director), together they have over 50 years of experience in this sector. They will be supported by a management structure headed by an Advisory Board chaired by Pascal Terrien, Director of the European Centre and Laboratories for Energy Efficiency Research and responsible for the Demand Reduction programme of the UK Energy Technology Institute. This will help secure the international, industrial and UK research linkages of the DTC.Students will receive a stipend that is competitive with other DTCs in the energy arena and, for work in certain areas, further enhancement from industrial sponsors. They will have a personal annual research allowance, an excellent research environment and access to resources. Both Universities are committed to energy research at the highest level, and each has invested over 3.2M in academic appointments, infrastructure development and other support, specifically to the energy demand reduction area. Each university will match the EPSRC funded studentships one-for-one, with funding from other sources. This DTC will therefore train at least 100 students over its 8 year life.

Do bacteria care about the seasons? Birds migrate, mammals hibernate, plants flower, insects undergo diapause: in fact, almost all branches of the eukaryotic tree of life have evolved responses that allow them to alter their behavior and physiology in anticipation of the changing seasons. This usually happens through a phenomenon called photoperiodism, in which the length of the day is the environmental factor responsible for triggering these changes. Photoperiodism is a well-studied phenomenon that underlies important events in an organism's life and its interactions with other species. It is also directly affected by climate change, as changes in temperature and other weather variables can render a previously beneficial photoperiodic response maladaptive. Establishing how photoperiodic responses will change under climate change is an imperative, but currently we lack model organisms that allow us to directly test this, as their generally lengthy life cycles have so far precluded attempts at experimentally evolving photoperiodism. During my PhD, I made the timely discovery that bacteria are also capable of photoperiodic responses. Similar to short-day induced hibernation in mammals, when cells of Synechococcus elongatus PCC 7942 - a remarkable cyanobacterial model organism within the field of circadian rhythms - are exposed to short, winter-like days, they are capable of surviving freezing temperatures 2-3x better than counterparts that are exposed to long, summer-like days. Throughout my PhD, I have physiologically characterized this response and learned that it functions rather similarly to eukaryotic photoperiodism, despite their vast phylogenetic distance. Remarkably, this response is dependent upon the presence of a functional circadian clock, takes multiple generations to be formed, and involves anticipatory changes in lipid membrane saturation. The overarching goal of this proposal is to harness this striking discovery and establish cyanobacteria as the first bacterial model for studying the evolution of photoperiodism. Due to their fast generational time, simple genome and systematically characterized circadian clock, cyanobacteria are a unique model organism that would allow us to not only determine the mechanistic features of photoperiodism, but also would make it possible to perform experimental evolution under various conditions. In this proposal, I intend to make this possible by three separate strategies. First, I will use the vast array of molecular tools available for Synechococcus and establish the genetic basis of cyanobacterial photoperiodism through RNAseq and transposon sequencing, as well as use proteomics to determine other responses beyond cold resistance that may also be photoperiodic. Second, I will test different cyanobacteria and other model bacteria to establish how phylogenetically widespread photoperiodism is amongst prokaryotes, and whether cyanobacteria could also be a model for the study of latitudinal clines. Finally, I will perform experimental evolution on cyanobacteria under climate change conditions based on the latest models proposed by the Intergovernmental Panel on Climate Change and establish the evolutionary pathways that cyanobacteria and other organisms might take as they try to adapt to the new environments forced upon them by climate change. Taken together, these aims will fast-forward the study of photoperiodism and its past and future evolution, providing new tools to understand and mitigate the effects of climate change upon photoperiodic responses in general.

Network science, the study of systems of interconnected entities and their functional interactions, has three principal goals: 1. Discover and enumerate the basic principles of networked systems. 2. Use structure, dynamics, and demographics to infer functional interactions when they are not directly prescribed. 3. Predict network structure and demographics, and use mathematical and computational methods to manipulate existing networks and design new networks with desired properties. Networks provide a powerful tool for representing and analysing complex systems of interacting entities. They arise in the physical, biological, social, and information sciences and can be used to represent interactions between proteins, friendships between people, hyperlinks between web pages, and so on. A network consists of a set of entities (called "vertices") that are connected to each other by ties (called "edges"). Most studies of networks consider static networks with a single type of edge, and numerous tools have been developed to study such networks. However, networks that arise in applications are often more complicated. They can be "dynamic" in that they can have a time-dependent structure, which might represent changes in the committee assignments or voting patterns of politicians over time or different functional connectivity of brain regions during different parts of a motor activity. They can also be "multiplex" in that they include multiple edge types, such politicians who are connected both via common committee assignments and similar voting patterns. Although researchers have long been aware that networks in applications are both dynamic and multiplex, it is only in the past few years that high-quality data has become available to study such situations effectively. I recently helped develop a "multislice" framework for networks, along with accompanying algorithmic tools, which can be used for studying time-dependent and multpliex networks (Mucha et al, Science, 2010). The multislice framework departs from the norm in network science, as it formulates networks using three-dimensional arrays of numbers instead of the usual adjacency matrices (i.e., two-dimensional arrays). The 2010 paper developed a tool in multislice networks for the algorithmic detection of structures known as "communities", each of which consists of a set of vertices that are connected more densely to each other than they are to vertices in the rest of the network. The presence of different types of network edges, which are interrelated and evolve in time, raises conceptual and practical questions about network structure, and the multislice framework can be used to try to answer them. The proof of principle in our 2010 paper paves the way to studying dynamic and multiplex networks in subjects such as biology and political science. However, applying this framework to applications in practice will require considerable effort on both conceptual and application-oriented fronts. The proposed programme will make major headway towards this goal, especially in the area of community structure. Through my collaborations (see Letters of Support), I have access to large data sets from political science and biology. Overcoming the challenging nature of dynamic and multiplex data will yield interesting insights both conceptually and for applications. Much is known about community structure in static networks with only a single type of edge, but almost nothing is understood about community structure in either dynamic or multiplex networks. Most networks encountered in applications have such features, and my proposal directly addresses this issue.

The project 'ENG-EPSRC EFRI ELiS: Developing probiotic interventions to reduce the emergence and persistence of pathogens in built environments' is an international, multidisciplinary research project that addresses contemporary agendas towards designing and buildings healthy built environments. The project brings together expertise in microbiology, the built environment, infectious disease and antimicrobial resistance (AMR). The proposal responds to the urgency for improving the health of our built environments using an approach that departs from the modern understanding that healthy environments should be based on fewer microbes. Urbanisation, indoor lifestyles and ingrained antibiotic mentalities are selecting for AMR and there is a risk that the current pandemic exacerbates our overreliance on antibiotic approaches which are driving other unintended, longer term public health problems. This approach considers a more nuanced understanding of microbes that recognises that not all microbes are pathogenic. In this manner, future healthy buildings should aim to discriminate between good and bad microbes and in doing so, find ways that can reduce exposure to harmful microbes but also permit the presence and agency of benign environmental microbes roles that are beneficial for human health and the resilience of buildings and cites. The proposal will develop novel probiotic materials for buildings that contain living strains of B.subtilis, a soil derived bacteria that exhibits mechanisms which can inhibit the growth of drug resistant organisms. In the laboratory, we will engineer these probiotic materials for application in buildings that can demonstrate long term survival and ability to prevent AMR bacteria colonisation on these materials and on other building surfaces. In the workshop we will develop novel bio-fabrication approaches that will allow for these living materials to be manufactured in to a series of 1:1 living building component prototypes. These prototypes which will include floor and wall surfaces, furniture components and building panels and cladding will undergo a longitudinal microbial study in a real world building environment at OME, HBBE at Newcastle University, addressing longer term questions of how to progress this approach for building application.

Artificial intelligence (AI) is on the verge of widespread deployment in ways that will impact our everyday lives. It might do so in the form of self-driving cars or of navigation systems optimising routes on the basis of real-time traffic information. It might do so through smart homes, in which usage of high-power devices is timed intelligently based on real- time forecasts of renewable generation. It might do so by automatically coordinating emergency vehicles in the event of a major incident, natural or man-made, or by coordinating swarms of small robots collectively engaged in some task, such as search-and-rescue. Much of the research on AI to date has focused on optimising the performance of a single agent carrying out a single well-specified task. There has been little work so far on emergent properties of systems in which large numbers of such agents are deployed, and the resulting interactions. Such interactions could end up disturbing the environments for which the agents have been optimised. For instance, if a large number of self-driving cars simultaneously choose the same route based on real-time information, it could overload roads on that route. If a large number of smart homes simultaneously switch devices on in response to an increase in wind energy generation, it could destabilise the power grid. If a large number of stock-trading algorithmic agents respond similarly to new information, it could destabilise financial markets. Thus, the emergent effects of interactions between autonomous agents inevitably modify their operating environment, raising significant concerns about the predictability and robustness of critical infrastructure networks. At the same time, they offer the prospect of optimising distributed AI systems to take advantage of cooperation, information sharing, and collective learning. The key future challenge is therefore to design distributed systems of interacting AIs that can exploit synergies in collective behaviour, while being resilient to unwanted emergent effects. Biological evolution has addressed many such challenges, with social insects such as ants and bees being an example of highly complex and well-adapted responses emerging at the colony level from the actions of very simple individual agents! The goal of this project is to develop the mathematical foundations for understanding and exploiting the emergent features of complex systems composed of relatively simple agents. While there has already been considerable research on such problems, the novelty of this project is in the use of information theory to study fundamental mathematical limits on learning and optimisation in such systems. Information theory is a branch of mathematics that is ideally suited to address such questions. Insights from this study will be used to inform the development of new algorithms for artificial agents operating in environments composed of large numbers of interacting agents. The project will bring together mathematicians working in information theory, network science and complex systems with engineers and computer scientists working on machine learning, AI and robotics. The aim goal is to translate theoretical insights into algorithms that are deployed onreal world applications real systems; lessons learned from deploying and testing the algorithms in interacting systems will be used to refine models and algorithms in a virtuous circle.