Fusion Power has the potential to solve one of society's greatest challenges: universal access to plentiful, safe & sustainable energy. A person's entire lifetime energy needs can be supplied from fusion energy using the deuterium taken from a domestic bath of water and the tritium that can be bred from the lithium in a single mobile phone battery. Fusion power plants cannot suffer any type of runaway and they do not produce any direct greenhouse gas emissions. However achieving fusion is technically challenging: it requires heating the deuterium & tritium fuel to millions of degrees. At this temperature, the fuel becomes a plasma - a gas of charged particles. The plasma must be confined for sufficient time at sufficient density in order to get more energy out than we put energy in. There are a number of approaches being explored but the most successful are (1) magnetic confinement fusion which holds the fuel by magnetic fields at relatively low density for relatively long times in a chamber called a tokamak, and (2) inertial confinement fusion which holds the fuel for a very short time but at huge densities. The exciting news is that fusion is now entering a golden era. Since 2020, there have been substantial scientific breakthroughs, such as at JET in the UK and at NIF in the US. There has been dramatic expansion into the private sector with over 30 companies globally pursuing a range of approaches and many more establishing the fusion supply chain; governments around the world, but especially in the UK, are investing to accelerate fusion delivery. A remaining critical barrier to making fusion a reality is the shortage of people who understand the inter-related operational constraints for both the plasma fuel and its containment materials, including the breeding of tritium from lithium, all of which must be satisfied simultaneously. The EPSRC CDT in Fusion Power will build on our existing success and international reputation to become the global beacon for training the next generation of fusion leaders. At the core of our CDT is the partnership between six leading research-intensive universities and more than 20 private companies, UK & international labs and government agencies. Our students will benefit from a systems-thinking-based technical training in plasma physics and materials science including tritium breeding & handling. They will benefit from training delivered by non-academic partners in topics such as regulation & licensing, commercialisation & entrepreneurship, sustainability, financing & investment and project management. Through the CDT partners, the students will use internationally leading experimental facilities and high performance supercomputers. Initially through their supervisors and then increasingly independently, students will access international networks of institutions and fusion professionals. During their PhD, students will have the opportunity to build their track record through presenting work at conferences and leading their own "collaboratory" mini project. These scientists and engineers will go on to solve the technical cross-disciplinary challenges, moving fusion forward faster at a rate of more than 20 scientists & engineers per year. We will increase diversity in the fusion community through: positive recruitment & admissions practices; supportive, cohort-based training activities; undergraduate fusion internships for students from under-represented groups; outreach to the public and via sustained relationships with target schools. This supply of the best people will energise the UK fusion industry and enable a global ambition for fusion power plant innovation & development.

Imagine a future where autonomous systems are widely available to improve our lives. In this future, autonomous robots unobtrusively maintain the infrastructure of our cities, and support people in living fulfilled independent lives. In this future, autonomous software reliably diagnoses disease at early stages, and dependably manages our road traffic to maximise flow and minimise environmental impact. Before this vision becomes reality, several major limitations of current autonomous systems need to be addressed. Key among these limitations is their reduced resilience: today's autonomous systems cannot avoid, withstand, recover from, adapt, and evolve to handle the uncertainty, change, faults, failure, adversity, and other disruptions present in such applications. Recent and forthcoming technological advances will provide autonomous systems with many of the sensors, actuators and other functional building blocks required to achieve the desired resilience levels, but this is not enough. To be resilient and trustworthy in these important applications, future autonomous systems will also need to use these building blocks effectively, so that they achieve complex technical requirements without violating our social, legal, ethical, empathy and cultural (SLEEC) rules and norms. Additionally, they will need to provide us with compelling evidence that the decisions and actions supporting their resilience satisfy both technical and SLEEC-compliance goals. To address these challenging needs, our project will develop a comprehensive toolbox of mathematically based notations and models, SLEEC-compliant resilience-enhancing methods, and systematic approaches for developing, deploying, optimising, and assuring highly resilient autonomous systems and systems of systems. To this end, we will capture the multidisciplinary nature of the social and technical aspects of the environment in which autonomous systems operate - and of the systems themselves - via mathematical models. For that, we have a team of Computer Scientists, Engineers, Psychologists, Philosophers, Lawyers, and Mathematicians, with an extensive track record of delivering research in all areas of the project. Working with such a mathematical model, autonomous systems will determine which resilience- enhancing actions are feasible, meet technical requirements, and are compliant with the relevant SLEEC rules and norms. Like humans, our autonomous systems will be able to reduce uncertainty, and to predict, detect and respond to change, faults, failures and adversity, proactively and efficiently. Like humans, if needed, our autonomous systems will share knowledge and services with humans and other autonomous agents. Like humans, if needed, our autonomous systems will cooperate with one another and with humans, and will proactively seek assistance from experts. Our work will deliver a step change in developing resilient autonomous systems and systems of systems. Developers will have notations and guidance to specify the socio-technical norms and rules applicable to the operational context of their autonomous systems, and techniques to design resilient autonomous systems that are trustworthy and compliant with these norms and rules. Additionally, developers will have guidance to build autonomous systems that can tolerate disruption, making the system usable in a larger set of circumstances. Finally, they will have techniques to develop resilient autonomous systems that can share information and services with peer systems and humans, and methods for providing evidence of the resilience of their systems. In such a context, autonomous systems and systems of systems will be highly resilient and trustworthy.

Cyber-attacks such as those recently perpetrated on Solarwinds, Colonial Pipeline and Viasat are scaling at an alarming rate. Resilient cyber security technologies are vital to ensure that society can safely and confidently adopt new digital technologies. As our world becomes increasingly digitally connected for utilities, travel, healthcare, education, and commerce, and with the increasing use of artificial intelligence, cyber-physical infrastructure and the commercialisation of space-based entities, new security vulnerabilities are also emerging. This for novel cyber security solutions and secure technology supply chains presents key opportunities for research, innovation and economic impact. Based at Queen's University Belfast, CSIT is a global research and innovation hub for cyber security, and the UK's Innovation and Knowledge Centre (IKC) for cyber security research. CSIT is therefore in a strong position to make further and significant contributions, maintaining the UK's international research reputation and enhancing its economic and business competitiveness. Through its unique open innovation model with trusted industry partners, CSIT is pioneering research and innovation to protect citizens and businesses and drive economic impact. CSIT's unique model of innovation incorporates a significant engineering and professional services capability differentiating it from other cyber security academic research centres. As a delivery partner of LORCA, the DCMS funded cyber security accelerator, CSIT supported the growth of 70+ UK cyber security companies through knowledge transfer and product development. CSIT has successfully delivered during IKC Phases 1 and 2, and over the next 5 years we will consolidate and raise our level of impact nationally and internationally, continuing to fulfil our key role linking industry, government and academic expertise to promote economic growth. Under the theme of "Securing Complex Systems", CSIT will research and develop new technologies, acting as a nucleating point to accelerate and promote disruptive business opportunities that arise for the wider benefit of the UK cybersecurity industry. This will enable CSIT to seed new research activity in emerging areas of cyber security including, Semiconductor Chip Security, Secure and Resilient Cyber-Physical Infrastructure, Securing Machine Learning, as well as targeting Space Security as a new sectoral focus, with the aim of attracting new funding to drive collaborative research and innovation in these areas. To raise our level of impact, CSIT will build Hubs of Impact with industry partners in one or more of the research areas identified above, modelled on the proposed 'Cyber-AI Technologies Hub' in which CSIT will partner with eight cyber security technology companies to collaborate on the development of new solutions to shared challenges. We conservatively estimate that the £3M investment for CSIT3 could help to unlock up to £10.7M in economic impact across the UK, facilitated by job creation through research projects, support for economic clusters across the UK, engineering support for start-ups and scale-ups, and through public engagement with potential investors to the UK. CSIT3 targets over the next 5 years include: (a) £12M in public research and innovation funding; (b) £900k in industry membership fees; (c) at least 5 examples of successful translation and IP activity from CSIT research; (d) 10 funded industry-academic collaborative projects; and (e) 1 Hub of Impact. Based on CSIT's track record, we fully expect to deliver additional impact beyond these targets and further strengthen the UK's reputation as a global leader in cyber security research and innovation.

Public opinion on complex scientific topics can have dramatic effects on industrial sectors (e.g. GM crops, fracking, global warming). In order to realise the industrial and societal benefits of Autonomous Systems, they must be trustworthy by design and default, judged both through objective processes of systematic assurance and certification, and via the more subjective lens of users, industry, and the public. To address this and deliver it across the Trustworthy Autonomous Systems (TAS) programme, the UK Research Hub for TAS (TAS-UK) assembles a team that is world renowned for research in understanding the socially embedded nature of technologies. TASK-UK will establish a collaborative platform for the UK to deliver world-leading best practices for the design, regulation and operation of 'socially beneficial' autonomous systems which are both trustworthy in principle, and trusted in practice by individuals, society and government. TAS-UK will work to bring together those within a broader landscape of TAS research, including the TAS nodes, to deliver the fundamental scientific principles that underpin TAS; it will provide a focal point for market and society-led research into TAS; and provide a visible and open door to engage a broad range of end-users, international collaborators and investors. TAS-UK will do this by delivering three key programmes to deliver the overall TAS programme, including the Research Programme, the Advocacy & Engagement Programme, and the Skills Programme. The core of the Research Programme is to amplify and shape TAS research and innovation in the UK, building on existing programmes and linking with the seven TAS nodes to deliver a coherent programme to ensure coverage of the fundamental research issues. The Advocacy & Engagement Programme will create a set of mechanisms for engagement and co-creation with the public, public sector actors, government, the third sector, and industry to help define best practices, assurance processes, and formulate policy. It will engage in cross-sector industry and partner connection and brokering across nodes. The Skills Programme will create a structured pipeline for future leaders in TAS research and innovation with new training programmes and openly available resources for broader upskilling and reskilling in TAS industry.

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.