The ambitious targets in the United Kingdom for increasing the share of renewable energy sources integrated to the network, and the need for providing affordable, resilient and clean energy, call for a paradigm shift in energy systems operations. Electric vehicles offer the means to address these challenges and achieve uninterrupted operation by deferring their demand in time and acting as dynamic storage devices. As a result, their number is expected to increase rapidly over the next years, leading to a "green car revolution". This constitutes an opportunity for modernizing energy systems operation, but will unavoidably give rise to coordination and scheduling issues at a population level so that cost savings are achieved and reliability is ensured. The latter is of significant importance to prevent from undesirable disruptions of service. This project will address this problem using tools at the intersection of control theory, optimization and machine learning, allowing for a decentralized computation of the electric vehicle charging strategies, while preventing vehicles from sharing information about their local utility functions and consumption patterns that is considered to be private. We will develop algorithms capable of dealing both with cooperative and non-cooperative vehicle behaviours in large fleets of vehicles, and immunize the resulting strategies against uncertainty due to unpredictability in the vehicles' driving behaviour and due to the presence of renewable energy sources. The presence of an algorithmic tool with these features will allow for scalable charging solutions amenable to problems of practical relevance, will provide insight on the mechanism driving the response of large populations of electric vehicles, and embed robustness in the resulting charging schedules. As such, the proposed project will offer the means for reliable system operation and facilitate the integration of higher shares of renewable energy sources.

Future wireless communication networks are expected to address unprecedented challenges to cope with a high degree of heterogeneity in terms of devices, deployment types, environments, carrier frequency, etc. Moreover, they are expected to provide orders of magnitude improvement to such heterogeneous networks in key technical requirements including throughput, number of connected devices, latency and reliability. With such diverse services and diverging requirements, it is cumbersome to design a unified all-in-one radio system to meet the technical needs for all types of services. In addition, designing separate systems that run on separate infrastructures make the operation and management of the system highly complex, expensive and spirally inefficient. The scope of the project is to establish a radio ecosystem on a common infrastructure that efficiently accommodates communication services for all vertical sections from manufacturing, entertainment, public safety, public transport, healthcare, financial services, automotive and energy utilities. This can be enabled by an algorithmic framework orchestrating all radio slices that are individually customised and optimally designed. Network slicing is an overarching feature towards 5G-and-beyond to support all scenarios efficiently. Core network slicing has attracted much attention through network functions virtualisation. However, from the radio level, an algorithmic framework for spectrum- and cost-efficient air-interface to achieve the true potential of end-to-end network slicing for the future diverse radio systems is still an open problem yet to be solved. To guarantee the required performance for each individual user case efficiently, the physical layer (PHY) configurations should be delicately optimised and medium access control layer (MAC) radio resource should be allocated on-demand. For instance, subcarrier spacing is one of the paramount importance parameters for modern multicarrier communication systems (e.g., LTE, WiFi, etc.), the service for future massive machine type communications (mMTC) might require smaller subcarrier spacing (thus larger symbol duration) to support massive delay-tolerant devices. While vehicle to vehicle (V2V) communications, on the other hand, have more stringent latency requirements, thus, symbol duration should be significantly reduced compared to mMTC. However, cohabitation of the individually optimised services in one system may bring several technical challenges from both PHY and MAC. It will destroy the system orthogonality and PHY algorithm framework that the state-of-the-art telecommunication systems built on. From the resource allocation perspective, one of the challenges is that not only the multi-slice system forests a complex multiple layers resource structure, but also technical requirement of each slice can be significantly different. Thus, a cross-layer and cross-slice optimisation is envisioned to maximise the overall air-inference performance. The aim of REORDER is to address the abovementioned challenges, by establishing the framework of air-interface heterogeneous signal orchestration and efficient resource allocation. The proposed work fills in the last piece of the puzzle for realistic and efficient end-to-end network slicing. From this sense, REORDER will "reorder" the radio resource allocation caused by slice configuration disorders. The project will be undertaken in the Communication, Sensing and Imaging research group (CSI) in the University of Glasgow, by the PI, a PDRA and a PhD student based at the University of Glasgow. Our industrial partners include NEC Telecom MODUS (UK), Mathworks Research Centre Glasgow, and VIAVI Solutions (UK). The radical approaches proposed in this project will be verified though both state-of-the-art standard compatible system-level simulation and software defined radio (SDR) based over-the-air experimentations.

We are increasingly dependent on complex "smart" systems: cities, houses, vehicles, electricity grids and a myriad of connected 'things' gathering information and performing automated decision-making with or without a human in the loop. This is in part possible because of technological advances in sensing, actuation, computer hardware, networking and communication, which enable the harnessing, processing and analysis of vast volumes of data. Major advances in Automatic Control Engineering have provided the underpinning theory, methodology and practice needed to design and implement highly complex control and decision-making systems. Automatic control engineering continues to play a vital role in realising the government's long-term industrial strategy of raising productivity and earning power within the UK. Specifically, automatic control is a key enabling technology for all four major societal challenge themes identified in the 2017 UK Industrial Strategy: AI and Data, Clean Growth, Future Mobility and Aging Society and the specific challenge areas within each theme. Automatic control not only dramatically improves the productivity, efficiency, reliability and safety of a wide range of processes across all sectors, but also provides fundamental theory, methodologies and tools to further the understanding and enable discovery in other disciplines such as biology, medicine and social sciences. Whilst the UK led the First Industrial Revolution through the adoption of new technologies, including automation and control, today it lags behind its international competitors. This is evidenced in part by the slow productivity growth over the past decade, which is in sharp contrast to other economic indicators. It is argued that if the UK does not make a concerted effort to transition towards automation, it will miss a pivotal opportunity for growth, estimated to be worth more than £200 billion to the UK economy by 2030. For the UK to become a global leader in intelligent automation and leapfrog international competitors, it is vital that it consolidates its research leadership in automatic control engineering. The UK has a strong control engineering community of well over 1000 active researchers, and engineering practitioners spanning all career stages, which are represented at an international level by the UK Automatic Control Council (UKACC), the United Kingdom's National Member Organisation (NMO) of the International Federation of Automatic Control (IFAC), acting as an effective link between the UK and the international control communities. At the time of dramatic advances in automation, AI, sensing and computation technologies, in order to engage effectively with the UK Grand Challenge research agenda, avoid fragmentation of effort and to ensure control engineers are engaged from the outset with end-users or initiatives, there is a need for the UK control community to connect effectively with other academic and industry stakeholders, to develop a common research vision and strategy and to start addressing these challenges through ambitious pilot studies, paving the way for full-scale, high-impact grant proposals, novel groundbreaking research and knowledge transfer projects. The Automatic Control Engineering Network aims to drive forward the UK's research and international leadership in next-generation automation and control, by bringing together and connecting the country's expertise in automation, the internet-of-things, cybersecurity, machine learning and robotics, with industry stakeholders and the wider research communities working towards addressing the same pressing societal challenges. Through the creation of a Virtual Centre of Excellence in Automation and Control, the Network will ensure that the coordination of research efforts, industry engagement, training activities and resource sharing needed to address Grand Challenges, will continue beyond the end of the funding period.

The DigitalMetal CDT is born out to meet a national, strategic need for training a new generation of technical leaders able to lead digital transformation of metals industry & its supply chain with the objective of increasing agility, productivity & international competitiveness of the metals industry in the UK. The metals industry is a vital component of the UK's manufacturing economy and makes a significant contribution to key strategic sectors such as construction, aerospace, automotive, energy, defence and medical, directly contributing £20bn to UK GDP, and underpins over £190bn manufacturing GDP. Without a new cadre of leaders in digital technologies, equipped to transform discoveries and breakthroughs in metals and manufacturing (M&M) technologies into products, the UK risks entering another cycle of world-leading innovation but losing the benefits arising from exploitation to more capable and better prepared global competitors. The evolution to Industry 4.0 and Materials 4.0 coupled with unprecedented opportunities of "big data" enable the uptake of artificial intelligence/deep learning (AI/DL) based solutions, making it feasible to implement zero-defects, right first-time manufacturing/zero-waste (ZDM/ZW) concepts and meet the environmental-, sustainable- and societal- challenges. However, to fully take advantage of these opportunities, two critical challenges must be addressed. First, as user-identified problems in the metals industry that spans domains (from discoveries in M&M to their up-scaling and deployment in high volume/value production), urgently needed a new breed of engineers with skills to traverse these domains by going beyond the classical PhD training, i.e., T-model signifying transferable skills and in-depth knowledge in a single domain, to a new Pi-model raining that is underpinned by transferable skills and in-depth knowledge that transverse across domains i.e.,: AI/DL and engineering (M&M) to enable rapid exploitation of discoveries in M&M. Second, while AI/DL domain provides data-driven correlation analysis critical for product performance and defect identification, it is insufficient for root cause analysis (causality). This necessitates training on integrating data-driven with physics-based models of product & production, which is currently lacking in the metals industry. The Midlands region, as the top contributor to UK Gross Value Added through metals and metal products, with world-leading companies, such as Rolls-Royce and Constellium, LEAR and their customers, underpinned through collaborations with the five Midlands universities: Birmingham, Leicester, Loughborough, Nottingham & Warwick, is uniquely positioned to integrate research and industry resources and train a new cadre of engineers & researchers on the Pi-model to address user-needs. Our vision is to train future leaders able to accelerate the exploitation of M&M discoveries using digital technology to enable defect-free, right first-time manufacturing at reduced costs, digitise to decarbonise, and implement fuel switching in metals manufacturing industry.

This CDT will create a cohesive, internationally-leading, cross-domain training and research community fusing algebraic, geometric and quantum methods across Algebra, Geometry and Topology, Mathematical Physics, and their Interfaces. The scientific aim of our CDT is no less than to develop new foundations unifying all three disciplines, and in the process to bolster and future-proof UK capability in mathematics. The breadth of mathematical mastery necessary to achieve these aims, on which our training programme is based, is of the highest international standard, and training students in this area requires both the deep focus and the wide scope which only the resources of a CDT can enable. Our three scientific areas Algebra, Geometry and Quantum Fields are established, flagship, internationally-leading areas of UK mathematical strength. Algebra: quite simply *the* language, and controlling structure, of symbolic computation and symmetry. Geometry: the mathematically rigorous foundations of our human spatial and visual intuition. Quantum Fields: the mathematical incarnation of our quantum physical reality. A hallmark feature of 21st century mathematics is the dramatically increased synergy and inter-dependence between these three fundamental disciplines. Whereas in centuries past mathematics and physics interacted primarily through analysis and calculus, the advent of quantum mechanics posits a fundamentally different, fundamentally algebraic, set of laws for the universe. Geometry enters irrevocably when we pose quantum mechanical laws in the presence of fields, such as the electro-magnetic and gravitational fields, which permeate throughout time and space. A surprising and thrilling discovery of 21st century mathematics has been that the mathematically rigorous study of quantum fields yields some of the most powerful predictive theories within algebra and geometry, even to questions with no a priori physical formulation. These fundamental scientific developments have had a vast and direct impact on our modern world, and on a remarkably short timescale. Algebra, geometry and quantum fields are the driving force behind key developments such as internet search, quantum computation, machine learning, and both classical and quantum cryptography. Society and industry need the students we will train. Our graduates' skills are both fundamentally transferable and widely applicable across many external partnerships and stakeholders. The Deloitte report, commissioned by EPSRC, attributed 2.8M jobs and £200BN of the UK economy to mathematical sciences research, highlighting R&D, computing/tech, public administration, defence, aerospace and pharmaceuticals as economic sectors requiring graduates with advanced mathematical training. Sustainable energy consulting has since emerged as a further industry requiring ever-advanced mathematical sophistication. Crucially a physical and mathematical powerhouse needs to be a diverse powerhouse, yet the traditional structure of training in these areas has inhibited diversity of entrants, both to career academia and to industry. Building on our track record, and equipped with the resources and flexibility only a CDT can provide, we will create a diverse and confident cohort, equipped with the mathematical skillsets needed for our tech-led future to flourish, and able to influence a wide range of people, sectors and institutions.