Global warming, environmental change/degradation and human activities have led to an unprecedented threat to the world's biodiversity. How to quantify and estimate biodiversity change - how biodiversity is changing over time - has become one of the most pressing issues in biology, ecology, evolution, environmental science, bioinformatics, and related research fields. Robust and meaningful diversity measures that possess good mathematical/statistical properties and support biological reasoning about diversity are required. To date, most of the effort has been directed towards quantifying taxonomic diversity - i.e. species relative abundance and composition. However, it is now recognized that biodiversity has multiple dimensions and that it is essential to consider phylogenetic and functional diversity as well. Fortunately, remarkable progress in our understanding of phylogenies and the extensive collection of species traits opens the door to innovative approaches to the measurement and assessment of biodiversity change. At the same time, this is a complex challenge; collaboration among ecologists and mathematicians/statisticians is essential to tackle it. This project will bring together world experts in the assessment of biodiversity, in a new collaboration. Its goal is to develop an integrated mathematical and statistical framework to quantify and estimate changes in taxonomic, phylogenetic and functional diversity, focusing on the BioTIME database. The focus of the work will be ecological assemblages, and how they change through time. Project partners are Sandra Diaz (Argentina), a world leader in quantifying functional diversity, and Anne Chao (leading the MOST component of the work) who is globally renowned for her statistical contributions to the quantification of biodiversity. They will collaborate with the UK (St Andrews) team (Anne Magurran and Maria Dornelas) who have pioneered the quantification of biodiversity change in taxonomic diversity. The new methodology will permit rigorous analysis of diversity changes for alpha, beta and gamma diversities based on all three dimensions of biodiversity (taxonomic, functional, phylogenetic). Access to the BioTIME database (biotime.st-andrews.ac.uk), currently the world's largest repository of assemblage time series, will provide a proof of concept of the methodology. We will also develop appropriate user-friendly, self-interpreting software, complete with online versions, and maintain a website featuring all software and statistical tools developed in this project. By offering a number of short visiting fellowships to postdocs, who will have an opportunity to work on key components of the analyses, we will increase the global reach of the collaboration. A workshop will provide a further opportunity to disseminate findings and secure the future of the collaboration. The project will thus forge a strong partnership between researchers who have not had the opportunity to work together in the past, while providing innovative solutions to an urgent ecological challenge.

Exploiting the laws of quantum mechanics for the benefit of society in the so-called "second quantum revolution" is one of the greatest challenges of 21st-century physics. With such capability, we would be able to make quantum technologies that allow secure communication, quantum computers that outperform supercomputers, and quantum simulators of complex physical problems inaccessible to solve with current computing technologies. In order for this to happen, we need to efficiently produce particles, control their states, detect them and make them interact strongly with each other. Photons, quantum particles of light, are one of the most promising building blocks of future quantum technologies. We can easily detect and control their states and we can efficiently produce them individually. However, making them interact strongly to build a large quantum network is a notoriously difficult task because photons do not interact at low energies. To make them interact indirectly, we can hybridise them with other particles that do strongly interact and form new particles called 'polaritons'. In this project, we aim to hybridise photons with Rydberg excitons. Rydberg excitons are highly excited electron-hole pairs that can span macroscopic dimensions. Because of their macroscopic dimensions they strongly repel each other. The semiconductor device that we have chosen for hybridisation is a 2-dimensional semiconductor microcavity formed by two highly reflective mirrors encompassing nanocrystals and thin films of cuprous oxide. Photons confined in the microcavity strongly couple to Rydberg excitons in cuprous oxide to form Rydberg polaritons. The Rydberg polaritons interaction strength will be orders of magnitude higher than the current microcavity polaritons. This breakthrough will allow us to explore quantum optics at the single-particle limit and form 2-dimensional networks of strongly correlated photons for future single-photon switches and quantum simulators.

This is currently one of the most exciting and dynamic periods for UK nuclear science & engineering since the 1950s. Inter alia, both new reactor build (essential to meet climate change targets) and the decommissioning of the UK's legacy nuclear sites (a 120 year, £121 bn programme) are driving forward, BEIS are investing heavily in the new national nuclear innovation programme and the sector deal for the industry has just been published. The already acute need for skilled nuclear scientists and engineers is therefore increasing and will continue to do so into the long term. To address these needs we propose a CDT in Nuclear Energy (GREEN), a partnership between 5 of the UK's leading nuclear universities and 12 industry partners, addressing EPSRC priority area 19: Nuclear Fission & Fusion for Energy. Evolving from the very successful Next Generation Nuclear (NGN) CDT, GREEN will deliver comprehensive doctoral training across the whole fission fuel cycle as well as in allied areas of fusion. Inspired by changes in external drivers and feedback from alumni, employers and funders, GREEN will offer both academically- and industrially- based research pathways, linked to enhanced employability training. We will further widen our already strong industry engagement by inclusion of new external partners, and align closely with other national and international activities, including other proposed CDTs. Experience from NGN suggests we will be able to leverage EPSRC support to give a typical cohort size of 15-20 students. Remarkably, using the leverage of 40 studentships from EPSRC, GREEN has already secured a further 47 studentships from Industry and Academia, ensuring a minimum number of 87 students in the GREEN CDT.

Risk is the potential of experiencing a loss when a system does not operate as expected due to uncertainties. Its assessment requires the quantification of both the system failure potential and the multi-faceted failure consequences, which affect further systems. Modern industries (including the engineering and financial sectors) require increasingly large and complex models to quantify risks that are not confined to single disciplines but cross into possibly several other areas. Disasters such as hurricane Katrina, the Fukushima nuclear incident and the global financial crisis show how failures in technical and management systems cause consequences and further failures in technological, environmental, financial, and social systems, which are all inter-related. This requires a comprehensive multi-disciplinary understanding of all aspects of uncertainty and risk and measures for risk management, reduction, control and mitigation as well as skills in applying the necessary mathematical, modelling and computational tools for risk oriented decision-making. This complexity has to be considered in very early planning stages, for example, for the realisation of green energy or nuclear power concepts and systems, where benefits and risks have to be considered from various angles. The involved parties include engineering and energy companies, banks, insurance and re-insurance companies, state and local governments, environmental agencies, the society both locally and globally, construction companies, service and maintenance industries, emergency services, etc. The CDT is focussed on training a new generation of highly-skilled graduates in this particular area of engineering, mathematics and the environmental sciences based at the Liverpool Institute for Risk and Uncertainty. New challenges will be addressed using emerging probabilistic technologies together with generalised uncertainty models, simulation techniques, algorithms and large-scale computing power. Skills required will be centred in the application of mathematics in areas of engineering, economics, financial mathematics, and psychology/social science, to reflect the complexity and inter-relationship of real world systems. The CDT addresses these needs with multi-disciplinary training and skills development on a common mathematical platform with associated computational tools tailored to user requirements. The centre reflects this concept with three major components: (1) Development and enhancement of mathematical and computational skills; (2) Customisation and implementation of models, tools and techniques according to user requirements; and (3) Industrial and overseas university placements to ensure industrial and academic impact of the research. This will develop graduates with solid mathematical skills applied on a systems level, who can translate numerical results into languages of engineering and other disciplines to influence end-users including policy makers. Existing technologies for the quantification and management of uncertainties and risks have yet to achieve their significant potential benefit for industry. Industrial implementation is presently held back because of a lack of multidisciplinary training and application. The Centre addresses this problem directly to realise a significant step forward, producing a culture change in quantification and management of risk and uncertainty technically as well as educationally through the cohort approach to PGR training.