BEIS recently launched the Innovation Strategy, which the Government will establish 'innovation missions' seeking to address global and UK challenges through innovation. The Government wants to focus on exploiting seven technology areas where the UK has global competitive strengths. The proposed research covers four out of seven areas including: Advanced materials and manufacturing; AI, digital and advanced computing; electronics, photonics, and quantum; and robotics and smart machines. Together with QinetiQ, QMUL have developed a radically broad but new concept as "software defined materials (SDMs)", for which properties can be modified by simply uploading and updating computer software. The impact of SDMs is huge and it leads to tight integration of sensing, actuation, and computation that biological systems exhibit to achieve shape and appearance changes, and tactile sensing at very high dynamic range (like birds in flight). The vision of DREAM Partnership is therefore to unlock fundamental research opportunities promised by SDMs through digital transformation which are centered on design and manufacturing of novel electromagnetic materials for the automation and reconfigurability of future wireless systems. The DREAM Partnership will provide added value to both organisations, including:- Benefits for QinetiQ: Refresh of their technology portfolio using state-of-the-art materials and devices; securing new business by enhancement of the applications in wireless communications; greater international competitiveness through innovation insertion into systems; co-development of IP for enduring benefits in multiple markets. Benefits for QMUL: Stronger and more engaged industrial partnerships; enhanced supervision by using external specialists from QinetiQ; Potential licensing income through innovation; Enhanced knowledge transfer and an applications centric focus aligned to UK industry requirements. Benefits for UK: Gain a foothold in the marketplace with a new technology; Establish a large supply chain through QinetiQ; Access export markets through new products with routes to markets established via QinetiQ and position the UK as a leader in a key growth sector to compete with overseas incumbents. QMUL has agreed a property deal with the Department of Health and Social Care (DHSC) that paves the way for the development of a Whitechapel Life Sciences Cluster in East London, a truly inclusive environment with culture diversity. We envisage that this new space development will house a number of cross-faculty research centres including the Centre of the Internet of Medical Things, which aligns strongly to areas of existing strength in the DREAM partnership. QMUL was one of the first universities to offer degree-level apprenticeships. We have been awarded £28m to lead an Institute of Technology offering degree-level apprenticeships in data-science and engineering with over 30 industrial partners. This provides QinetiQ a ready framework to trial our pilot with people from non-academic routes. QMUL has recently established the Institute for the Digital Environment, investing £3m to establish a University Enterprise Zone incubating digital-health businesses. This provides the space and connectivity with QinetiQ, and offers a critical-mass to test our approach. We will invest a significant of time and effort on developing a body of innovative work on equality, diversity and inclusion (EDI) and resposnsible research and innnovation (RRI), particularly from safe AI and digital manufacturing impact on future workforce linking with QinetiQ ethics and code-of-conduct approaches. Finally, The DREAM Partnership will provide UK the opportunity not only to sustain this talented group with its legacy of more than 50 years of antenna and electromagnetics research innovation, but also to develop technologies relevant to wireless communications and and resilient infrastructures, which are beneficial to all citizens in the UK.

With the exponentially increasing prevalence of networked sensors and other devices for collecting data in real-time, automated data analysis methods with theoretically justified performance guarantees are in constant demand. Often a key question with such streaming data is whether they show evidence of anomalous behaviour. This could, e.g., be due to malignant bot activity on a website; early warning of potential equipment failure or detection of methane leakages. These and other motivating examples share a common feature which is not accommodated by classical point anomaly models in statistics: the anomaly may not simply be an 'outlying' observation, but rather a distinctive pattern observed over consecutive observations. The strategic vision for this programme grant is to establish the statistical foundations for Detecting Anomalous Structure in Streaming data settings (DASS). Discussions with a wide-range of industrial partners from different sectors have identified important, generic challenges that cut across distinct DASS applications, and are relevant for analysing streaming data more broadly: Contemporary Constrained Environments: Anomaly detection is often performed under various constraints due, for example, to the restrictions on measurement frequency, the volume of data transferable between sensors and a central processor, or battery usage limits. Additionally, certain scenarios may impose privacy restrictions when handling sensitive data. Consequently, it has become imperative to establish the mathematical underpinning for rigorously examining the trade-offs between, e.g., statistical accuracy, communication efficiency, privacy preservation and computational demands. Handling Data Realities: A substantial portion of research in statistical anomaly detection operates under the assumption of clean data. Nevertheless, real-world data typically exhibit various imperfections, such as missing values, labelling errors in data streams, synchronisation discrepancies, sensor malfunctions and heterogeneous sensor performance. Consequently, there is a pressing need for the development of principled, model-based procedures that can effectively address the features of real data and enhance the resilience of anomaly detection methods. Identifying, Accounting for and Tracking Dependence: Not only are data streams often interdependent, but also anomalous patterns may be dependent across those streams. Taking into account both types of dependence is crucial in enhancing the statistical efficiency of anomaly detection algorithms, and also in controlling the errors arising from handling a large number of data streams in a principled way. Other challenges include tracking the path of an anomaly across multiple data sources with a view to learning causal indicators allowing for precautionary intervention. Our ambitious goal of comprehensively addressing these challenges is only achievable via the programme grant scheme. Our philosophy is to tackle the methodological, theoretical and computational aspects of these statistical problems together. This integrated approach is essential to achieving the substantive fundamental advances in statistics envisaged, and to ensuring that our new methods are sufficiently robust and efficient to be widely adopted by academics, industry and society more generally.

Lancaster University, together with a formidable consortium of industrial and third-sector partners, proposes a Centre for Doctoral Training (CDT) aimed at cultivating international research leaders in Statistics and Operational Research (STOR) through a programme in which real-world challenge is the catalyst for cutting-edge methodological advancement. Our partners face a challenging reality: the demand for highly-trained STOR data specialists consistently exceeds the available supply. This situation is exacerbated by the ever-growing significance of data in both the economy and society. Our proposal directly addresses this pressing demand, focussing on the priority area "meeting a user-need". The newly envisioned Centre builds upon the strengths and knowledge derived from an existing, internationally recognised EPSRC CDT. Expanding upon this foundation and with the input of an enlarged partner network, including blue-chip companies, SMEs, and third-sector organisations, we propose a Centre poised to recruit and train 70 students across five cohorts. This program will harness industrial and charitable challenges as inspirational springboards for conducting the highest calibre research. The new programme will innovate by * Developing a new MRes programme co-designed and delivered with our partners; * Including a comprehensive training programme on advanced, reproducible programming for STOR, co-ordinated by the Centre's dedicated, industry-funded, Research Software Engineer; * Embedding industrial and third-sector collaboration throughout the student experience; * Hosting seeded research clusters: vibrant, cross-cohort, cross-sector retreats to explore and develop early-stage challenges emerging from the shared interests of STOR-i and its partners; * Developing an ambitious doctoral exchange programme with highly regarded international university partners, comprising student exchanges, co-supervision and shared training activities. Our partners play an integral role in the Centre's plans, with 80% of doctoral projects adopting a CASE-like approach, receiving co-funding and co-supervision from industrial partners. All other students will engage in industrial research internships. Additionally, partners will lead problem-solving events, data immersion experiences, and contribute to Continuing Professional Development (CPD) activities such as leadership talks, fireside chats, and advanced programming training. The partnership is deeply committed to ensuring the broader impact of STOR-i as a national resource. To this end, the Centre will establish a suite of funded activities open to all UK STOR doctoral students. These include an annual STOR summer school with an emphasis on leadership skills, advanced programming, and a data dive focused on charitable endeavours. Additionally, students will have access to masterclasses and research visits. STOR-i will deliver a wide range of benefits and scientific outcomes to the end-user community, underpinned by three fundamental pillars: 1. People: Our CDT will inject 70 highly talented, diverse PhD graduates into the field, armed with the technical, interpersonal, and leadership skills essential for flourishing careers in STOR across a range of sectors. These graduates will serve as catalysts for innovation, driving cutting-edge research, and enhancing the UK's economic competitiveness. 2. Knowledge: The CDT will generate a wealth of cutting-edge research, disseminated in top STOR journals, and presented at major international conferences. This research will tackle substantial real-world challenges, yielding fresh insights and breakthroughs in STOR. 3. Impact: Our CDT will make a tangible difference in society and the economy by producing (i) case studies and (ii) a repository of documented and reproducible software, available to the public. This will facilitate widespread adoption of our research, leading to meaningful societal and economic impact.