This project (STRIDE) addresses the issue of how to make software development more resilient to constant changes of technology, staff, methods, requirements, expectations, regulations and more. The specific problem for this project is to characterise how automation can best be used to improve socio-technical resilience. The solution, based on interdisciplinary research, will be to provide: instruments for organisations to assess their resilience; and case studies, best practices, guidance and a concrete example (from automated fault localisation) to understand how humans and tools can best work together. In addition, we will advocate for a positive image for software engineering. So, STRIDE will investigate resilience and automation in the socio-technical system that supports software development, a system that includes people (engineers, users, managers), technical infrastructure (tools, development environments), processes (lean, requirements elicitation) and artefacts (code, wiki, coding standards). Breakdowns in socio-technical systems can cause significant disruption and Resilience Engineering aims to avoid them by emphasising what works, so that resilience can be preserved. From this perspective, resilience is defined as the productive tension between stability and change, always with the aim of producing systems that are "safe". This view of socio-technical systems is pertinent to modern software engineering where change has become endemic: with changing requirements, advanced technologies, complex infrastructure and new security threats. In addition to the constantly changing environment, software production is increasingly being automated, which requires repeated re-balance of this tension. But what is the relationship between resilience and automation? While improvements to software development brought by automation are vital to keeping software safe and secure, automation is not a silver bullet. It is said that "Making a system safer involves coupling the capabilities of humans with the technology they work with so that they can stay in control". What does that mean for software development? Is there something fundamentally human that needs to be retained as part of the software development process? And if so, how can a productive and resilient balance between human control and automation be maintained in the context of constantly increasing automation? How can automation be used to increase socio-technical resilience and what will be the impact on resilience of different levels of automation? STRIDE aims to address these and related questions. The project will determine and operationalise factors that indicate socio-technical resilience (STR) of software development, drawing on social psychology and resilience engineering, and grounding the research in the concrete development task of automated fault localisation. We will engage with representatives of two developer communities: commercial software engineers and professional end user developers who represent two different development environments. This work will have particular implications for improving STR and the pace and nature of automation in the software development lifecycle.

Climate and environmental (CE) risks (CER) to our economy and society are accelerating. CER include climate-related physical risks such as floods, storms, or changing growing seasons; climate-related transition risks such as carbon pricing and climate litigation; and environmental risks such as biodiversity loss. It is now well accepted that CER can impact asset values across multiple sectors and pose a threat to the solvency of financial institutions (FIs). This can cause cascading effects with the potential to undermine financial stability. The adoption of CER analytics will ensure that CE risks can be properly measured, priced, and managed by individual FIs and across the financial system. This is also a necessary condition to ensure that capital is allocated by FIs towards technologies, infrastructure, and business models that lower CER, which are also those required to deliver the net zero carbon transition, climate resilience, and sustainable development. These twin tracks - greening finance and financing green - are both enabled by CER analytics being appropriately used by FIs. The UK is a world-leader in Green Finance (GF). UK FIs have played a key role in GF innovation. Yet, despite these advances and leadership in almost every aspect of GF, UK FIs cannot secure the data and analytics needed to properly measure and manage their exposures to CER. While the last decade has seen the exponential growth of CE data, as well as improved analytics and methods, often produced by world-leading UK science, the vast majority of this has not found its way into FI decision-making. Our vision for CERAF is to establish a new national centre to resolve this disconnect. CERAF aims to enable a step-change in the provision and accessibility of data, analytics, and guidance and accelerate the integration of CER into products and decisions by FIs to manage CER risks and drive efficient and sustainable investment decisions, thereby delivering the following impacts: - Enhance the solvency of individual FIs in the UK and globally and so contribute to the resilience of the global financial system as a whole for all, as well the efficient pricing and reallocation of capital away from assets at risk to those that are more resilient. - Underpin the development and the growth of UK GF-related products and services. - Enable a vibrant ecosystem of UK enterprises providing CER analytics and realise the opportunity for UK plc of being a world-leader in the creation and provision of CER services. Our vision is that CERAF will be the nucleus of a new national centre established to deliver world-leading research, information, and innovation to systematically accelerate the adoption and use of CER data and analytics by FIs and to unlock opportunities for the UK to lead internationally in delivering CER services to support advancements in greening finance and financing green globally It aims to overcome the following barriers: 1) Making existing data on hazards, vulnerabilities, and exposures more accessible and useable for FIs, with clearly communicated confidence and with analytics that does not yet exist being secured; 2) Consistency and standards to reduce fragmentation, facilitate innovative products and enable the efficient flow and use of data; 3) Assurance and suitability are needed to understand which CER analytics are best suited for particular uses and provide transparency into underlying data and methodologies, so that CER analytics can be trusted and used; 4) Unlocking innovation through supporting FIs to test new approaches in a lower-risk way; and 5) Building capability, knowledge, and skills within FIs to analyse and interpret CER data. Resolving these barriers is a necessary condition for repricing capital and avoiding its misallocation, and achieving the UK's ambitions on GF.

The Centre for Doctoral Training MAC-MIGS will provide advanced training in the formulation, analysis, and implementation of state-of-the-art mathematical and computational models. The vision for the training offered is that effective modern modelling must integrate data with laws framed in explicit, rigorous mathematical terms. The CDT will offer 76 PhD students an intensive 4-year training and research programme that equips them with the skills needed to tackle the challenges of data-intensive modelling. The new generation of successful modelling experts will be able to develop and analyse mathematical models, translate them into efficient computer codes that make best use of available data, interpret the results, and communicate throughout the process with users in industry, commerce and government. Mathematical and computational models are at the heart of 21st-century technology: they underpin science, medicine and, increasingly, social sciences, and impact many sectors of the economy including high-value manufacturing, healthcare, energy, physical infrastructure and national planning. When combined with the enormous computing power and volume of data now available, these models provide unmatched predictive tools which capture systematically the experimental and observational evidence available. Because they are based on sound deductive principles, they are also the only effective tool in many problems where data is either sparse or, as is often the case, acquired in conditions that differ from the relevant real-world scenarios. Developing and exploiting these models requires a broad range of skills - from abstract mathematics to computing and data science - combined with expertise in application areas. MAC-MIGS will equip its students with these skills through a broad programme that cuts across disciplinary boundaries to include mathematical analysis - pure, applied, numerical and stochastic - data-science and statistics techniques and the domain-specific advanced knowledge necessary for cutting-edge applications. MAC-MIGS students will join the broader Maxwell Institute Graduate School in its brand-new base located in central Edinburgh. They will benefit from (i) dedicated academic training in subjects that include mathematical analysis, computational mathematics, multi-scale modelling, model reduction, Bayesian inference, uncertainty quantification, inverse problems and data assimilation, and machine learning; (ii) extensive experience of collaborative and interdisciplinary work through projects, modelling camps, industrial sandpits and internships; (iii) outstanding early-career training, with a strong focus on entrepreneurship; and (iv) a dynamic and forward-looking community of mathematicians and scientists, sharing strong values of collaboration, respect, and social and scientific responsibility. The students will integrate a vibrant research environment, closely interacting with some 80 MAC-MIGS academics comprised of mathematicians from the universities of Edinburgh and Heriot-Watt as well as computer scientists, engineers, physicists and chemists providing their own disciplinary expertise. Students will benefit from MAC-MIGS's diverse network of more than 30 industrial and agency partners spanning a broad spectrum of application areas: energy, engineering design, finance, computer technology, healthcare and the environment. These partners will provide internships, development programmes and research projects, and help maximise the impact of our students' work. Our network of academic partners representing ten leading institutions in the US and Europe, will further provide opportunities for collaborations and research visits.