When certain solid materials (for example, tin) are cooled down to very low temperatures, the electrons they contain start to behave not as individual, independent particles, but as a collective, collaborative entity, a kind of gas of electron pairs. This allows them to move without friction so that electrical currents can pass through the material with absolutely no energy loss. This phenomenon, called superconductivity, has immense technological potential, already partially exploited (most medical MRI scanners use superconducting magnets nowadays, for example). A major barrier to further exploitation is the very low temperatures at which superconductivity typically occurs (around -270 degrees C), which require refrigeration with liquid helium. Since mid 2001 complex materials have been engineered which exhibit superconductivity at relatively high temperatures and have several different inter-pervading collaborative electron "gases". Whereas the underlying mechanism for conventional low temperature superconductivity is well understood, the basis of superconductivity in these newer multiband materials is, so far, relatively mysterious. The aim of this project is to make a thorough mathematical study of a class of models of multiband superconductors called multicomponent Ginzburg-Landau models. The precise mathematical structure of the model is determined by underlying assumptions about the electron pairing mechanisms which lead to superconductivity. These models possess mathematically interesting solutions called "topological solitons", smooth spatially localized lumps of energy which cannot be dissipated by any continuous deformation of the system. The idea is to determine how the properties (the most important property being existence and stability) of these solitons depend on the mathematical structure of the model. The absence, presence and characteristics of these solitons in real superconductors can then be used to infer information about the electron pairing mechanisms underlying superconductivity in these materials.

Methane is a powerful greenhouse gas which significantly contributes to global warming. This gas is produced by microbes that live in environments that lack oxygen (anoxic). We have known for some time that marine environments produce substantial amounts of methane. However, the sources of methane in marine ecosystems have not been fully described, which is a barrier to correct calculation of methane emissions from these environments. In particular, we know very little about which microbes produce methane and what metabolic process they use. Previous studies showed that microbes can use dimethylsulphide (DMS) to grow and produce methane. DMS is a gas, which can be found in very high concentrations in marine environments. DMS in these habitats is produced through the breakdown of another compound shortly called DMSP, which is released in huge amounts following a phytoplankton bloom. The Baltic Sea is a unique environment. This is because it is one of the largest brackish (moderate to low salinity) seas in the world. It is also subjected to regular phytoplankton blooms. Overall, the Baltic Sea provides an excellent natural laboratory to study DMS use and methane generation by microbes. The brackish condition of the Baltic Sea is particularly important. Because, sulphate is one of the important ions that determine the salinity. Low-to-moderate salinity means there is sulphate available to microbes. This may however affect the activity of methane-producing microbes. This is because methane-producers compete with sulphate-users for carbon sources, in our case for DMS. Depending on the outcome of this interaction, the amount of methane produced in marine sediments may reduce significantly. Therefore, we aim to understand how this metabolic pathway works and which microbes are responsible of this process. In order to achieve our aim, we initiate a new partnership with colleagues from Sweden, who have long-term experience in the Baltic Sea research and tools to analyse critical data using powerful computing facilities. We will use our novel microbial ecology approach that combines state-of-the-art techniques with advanced microbial identification tools called high-throughput sequencing. Firstly, we will determine the extent to which DMS contributes to methane production in anoxic, brackish Baltic Sea sediments. We will then use isotopically labelled DMS, which enables us to follow the fate of carbon in sediments. Then, we will use genetic material (DNA and RNA) from microbes in the labelled sediment samples to identify the microbes that use DMS (methane-producing or sulphate-using) and infer their metabolism. The results will tell us the magnitude of methane production via DMS, which microbes use DMS and produce methane and how they carry out this process in brackish conditions in the Baltic Sea sediments. Overall, the outcome of this project will greatly improve our understanding of methane production in marine sediments and help in calculating greenhouse gas budgets via improved climate models. This will ultimately help us tackling global warming and climate change.

Fluid mechanics is of fundamental importance and underpins key developments in a range of disciplines, including aerospace, defence, energy and environmental research. For example, the efficient use of fuel has become an increasingly important factor in civil aviation, with the International Air Transport Association (IATA) committed to "reducing fuel consumption and CO2 emissions by at least 25% by 2020, compared with 2005 levels". Concurrently, aircraft engine noise has become a real and growing environmental issue, especially in the vicinity of airports around the world. Given the global demand for increased air travel, energy efficient and quieter aeroengines are important targets for the aviation and aerospace industries. Aerodynamic improvements have the potential to contribute to the design of the next generation of energy-efficient aeroengines and tubomachinery. Specifically, an increased understanding of the underlying physics through active flow control can reduce aerodynamic drag and delay the transition to unstructured, turbulent flow, both of which are known to have negative implications for fuel consumption and noise emissions. The goal of delaying turbulent-transition can be achieved in a cost-effective way through detailed numerical simulations, as opposed to comparatively expensive experimental investigations. This project will help to achieve that goal by applying a novel computational approach that can model flow within the boundary layer over complex rotating geometries. The boundary layer, a thin layer of fluid confining its viscosity close to a bounding surface, can influence the aerodynamics and drag characteristics of a fluid flow in a profound and significant way. Historically, the boundary layer flow over a rotating disk was used to model air flow over a swept-wing due to the similarity between their velocity profiles. Today, continuing developments in aeroengines, turbomachinery, spinning projectiles and, more recently, electrochemical applications, has created the need to understand boundary-layer flows over rotating bodies, such as disks, spheres and cones. Indeed, rotating 3D boundary-layer flows are now known to exhibit numerous flow characteristics, governed by highly complex and often competing mechanisms that cause flow instability and eventual breakdown to turbulent flow. For a family of rotating cones, experiments have observed a continuous change of flow characteristics as the governing flow parameters are altered. The applicant has shown that this change arises from an interaction between various forces governing flow within the boundary layer. However, the nature of this competing interaction remains largely unknown. With this in mind, this research project will develop a complex computational modelling code capable of providing robust and accurate quantitative predictions of the interaction between competing flow instability mechanisms in the turbulent-transition process. Such predictions can help to accelerate aerodynamics research, and inform or form part of innovative flow control strategies to reduce drag, thereby improving fuel consumption, as well as decreasing harmful noise and CO2 emissions.

This network aims to identify core questions that will drive forward the next phase in data-rich music research, focused in particular on creative music making. The increased availability of digital music data combined with new data science techniques are already opening new possibilities for making, studying and engaging with music. This direction is only likely to speed up upending many current practices, opening up creative avenues and offering new opportunities for research. However, the rapid technological progress with new techniques producing surprising results in rapid succession, is often disconnected from the knowledge and knowhow gained by musicians through creativity, practice and research. By bringing together researchers and practitioners from different underlying disciplines and with a wide range of expertise the network will enable a better foundation for future research. Performers, composers, and improvisers will contribute through embodied knowledge and practice-based methods; researchers in psychology will bring insights about cognitive, affective and behavioural processes underpinning musical experience; and data scientists will add analytical expertise as well as relevant theories, methods and techniques. These will lead to significant conceptual breakthroughs in data driven approaches and technologies applied to music. The new data-based technologies usually rely on large data sets, they can also produce very large amounts of data. As part of the network activities we will map the limitations of existing music representations, identify the gaps that need to be addressed and propose pathways to improve representation formats. We do not envision developing a single, all encompassing representation that captures the full richness of musical experience. Nevertheless, through the dialogue that this network will facilitate we will be able to outline ways of improving on existing representation formats and develop methods for visualising, analysing, and interpreting large data sets. The network will also consider ethical and legal implications such as how best to address the challenges that Artificial Intelligence (AI) poses to existing musical practices and the fear that this technology induces. Some of these are common to many fields where AI is being applied to tasks which were until very recently the preserve of humans. Music offers a unique perspective on these wider problems - the opacity of 'black box' generative models is a low-risk research challenge not a potentially dangerous tool that may entrench existing injustices. By embedding the ethical dimension into the discussion of the future of data driven music research the network will serve as a model for other fields. The core activity of the network are two workshops where short presentations will provide a springboard for in-depth discussions; a concert by practitioners with relevant experience will help connect the theoretical discussions to the reality of music making. These will enable a multidimensional exchange of ideas and methods. Material from these workshops will be shared online to document the process and provide a platform to engage wider audiences with the approach taken and the significant results obtained. Data driven technologies are already having an effect on the way in which we understand, make and consume music within current cultural and economic contexts, raising complex and unprecedented ethical and legal considerations. This network will identify core questions that can propel forward data driven research into creative music making that consider social and individual needs. We will also be able to outline specific research projects that address the shared concerns and bridge the gaps between the different methods that, in many ways, bound our disciplines. The network builds on previous AHRC funded research by the PI (AH/N504531/1 and AH/R004706/1) applying data to creative music making in a particular domain.

Access to safe drinking water is centrally linked to public health, well-being and economic prosperity. Although water quality is strongly linked to many of the UN Sustainable Development Goals (SDG 6: Clean Water & Sanitation, 3: Good Health & Well-Being, 5: Gender Equality, and 2: Zero Hunger), there is still a long way to go to achieve equitable access to safe drinking water, particularly in the Global South. To accelerate progress, we need new interdisciplinary approaches to tackle complex water quality challenges, especially with increasing stressors like rapid urbanisation and climate change impacting groundwater resources widely used for drinking. The aim of my FLF is to create a roadmap towards improved groundwater quality management in the context of the Global South by bringing together systematic approaches to improve the understanding of dominant groundwater processes and to support evidence-based decision-making for effective groundwater remediation. We will develop and demonstrate this approach in relation to two selected contrasting locations in South Asia (e.g. Bihar, India) and East Africa (e.g. Uganda) and for selected priority groundwater contaminants relevant to those locations. The roadmap approach developed here could then be applied to different scenarios in the future. We will bring together expertise in groundwater pollution (e.g. chemical, microbial, emerging contaminants, antimicrobial resistance), (bio)geochemical processes, remediation technologies, machine learning, decision science (e.g. agent based modelling, multi-criteria decision analysis) and social science to address local water quality and remediation challenges in these two areas. We will co-design decision tools, iteratively integrating scientific data with modelled predictions, to enable informed, locally-relevant decision-making for effective groundwater remediation. We will address an integrated set of key objectives and hypothesis (see objectives) through a series of Workpackages (WP) implemented as: (i) WP 1: Field-based Investigations comprising of WP 1.1 Multipollutant & Process Investigation and WP 1.2 Community Science; (ii) WP 2: Lab-based Investigations comprising of WP 2.1: Water & Sediment Characterisation and WP 2.2 Remediation Evaluation; (iii) WP 3: Predictive Modelling comprising of WP 3.1 Machine Learning; WP 3.2 Agent Based Modelling; and WP 3.3 Multi-Criteria Decision Analysis; and (iv) WP 4: Synthesis & Communication comprising of WP 4.1 Stakeholder Engagement and WP 4.2 Open Resource Bank Development. Our project team brings together highly complementary expertise and skillsets. I am an environmental engineer with expertise in groundwater pollution and remediation, with substantial experience managing and implementing complex, multi-partner research projects in South/Southeast Asia, Africa and South America. I am joined by Co-Investigators from The University of Manchester, British Geological Survey, University of Birmingham and University of Bath, along with international Project Partners from University of Melbourne (Australia), KTH Royal Institute of Technology (Sweden), Mahavir Cancer Sansthan (India), University of Heidelberg (Germany), Mbarara University of Science and Technology (Uganda) and independent affiliates from India and Malaysia. Collectively we bring together decades of interdisciplinary expertise in water science, remediation, water management, water and health, biotechnology, decision science, social science, participatory science, stakeholder engagement and extensive local knowledge in India and East Africa. The results and tools generated will improve the understanding of the complex natural and anthropogenic processes impacting groundwater quality in the selected locations and will better enable evidence-based decision making for effective groundwater remediation, with the roadmap generated able to be applied to other scenarios in the future.