The Milky Way-Gaia Doctoral Network (MWGaiaDN): Revealing the Milky Way (MW) with Gaia - Excellent science, Extending techniques, Enhancing people skills, Effecting the next revolution in European led astronomy through leadership in astrometric-based science. What: Gaia, ESA's major space mission launched in Dec 2013, is now in its extended mission to map some two billion stars in the MW. It's upcoming data releases , that will provide chemical and physical annotation of the earlier positional releases, present major challenges in terms of complexity and size, hence research training to deliver a full science exploitation is essential, ensuring that Gaia is the `game changer' for astronomy How: Our DN will link major partners responsible for the development of Gaia, to form an effective and unique training network combining the best research training with a range of academic and industrial placements, specialist research and knowledge transfer workshops. It will develop and train a cohort of young researchers through a set of key science projects pushing the Gaia data to its limits. Our DN will train 10 ESRs located across 10 European beneficiaries, benefiting from the participation of 13 associate partners. These include major industry (e.g. AirbusDS, TAS), at the forefront of Space and Information technologies; SME Industry (e.g. DAPCOM, Suil), innovating new technologies for Space and partners leading the development of next generation astrometry missions outside of Europe (NAOJ). Relevance: It will shape the delivery of training in astrometry and the study of the MW across Europe: delivering key insights into the structure and formation of our Galaxy; delivering the roadmap for the next generation of astrometric space telescopes; equipping the ESRs with skills to drive the next innovative steps in this crucial area of space discovery, as well as enabling them to contribute to the future, growth and challenges of the big data industry and commerce. MWGaiaDN

Standard multi-kW fibre lasers are now considered 'commodity' routinely produced by multiple manufacturers worldwide and are widely used in the most advanced production lines for cutting, welding, 3D printing and marking a myriad of materials from glass to steel. The ability to precisely control the properties of the output laser beam and to focus it on the workpiece makes high-power fibre lasers (HPFLs) indispensable to transform manufacturing through adaptable digital technologies. As we enter the Digital Manufacturing/Industry 4.0 era, new challenges and opportunities for HPFLs are emerging. Modern product life-cycles have never been shorter, requiring increased manufacturing flexibility. With disruptive technologies like additive manufacturing moving into the mainstream, and traditional subtractive techniques requiring new degrees of freedom and accuracy, we expect to move away from fixed, 'fit-for-all' beams to 'on-the-flight' dynamically reconfigurable 'shaped light' with extensive range of beam shapes, shape frequency and sequencing, as well as 3D focus steering. It is also conceivable that the future factory floor will get 'smarter', undergoing a rapid evolution from dedicated static laser stations to robotic flexible/reconfigurable floorplans, which will require 'smart photon delivery' over long distances to the workpiece. Such a disruptive transition requires a new advanced generation of flexible laser tools suitable for the upcoming 4th industrial revolution. Light has four characteristic properties, namely wavelength, polarization, intensity, and phase. In addition, use of optical fibres enables accurate control and shaping in the spatial domain through a variety of well-guided modes. Invariably, all photonic devices function by manipulating some of these properties. Despite their acclaimed success, so far HPFLs are used rather primitively as single-channel, single colour, mostly unpolarised and unshaped, raw power providers and remain at a relatively early stage (stage I) of their potential for massive scalability and functionality. Moreover, further progress in fibre laser power scaling, beam stability and efficiency is hindered by the onset of deleterious nonlinearities. On the other hand, the other unique attributes, such as extended 'colour palette', extensively controllable polarisation and beam shaping on demand, as well as massive 'parallelism' through accurate phase control remain largely unexplored. Use of these characteristics is inherent and comes natural to fibre technology and can add unprecedented functionality to a next generation of 'smart photon engines' and 'smart photon pipes' in a stage II of development. This PG will address the stage II challenges, confront the science and technology roadblocks, seek innovative solutions, and unleash the full potential of HPFLs as advanced manufacturing tools. Our aim is to revolutionise manufacturing by developing the next generation of reconfigurable, scalable, resilient, power efficient, disruptive 'smart' fibre laser tools for the upcoming Digital Manufacturing era. Research for the next generation of manufacturing tools, like in HiPPo PG, that will drive economic growth should start now to make the UK global leaders in agile laser manufacturing - enabling sustainable, resource efficient high-value manufacturing across sectors from aerospace, to food, to medtech devices and automotive. In this way the UK can repatriate manufacturing, rebalance the economy, create high added-value jobs, and promote the green agenda through efficient manufacturing. It will also enhance our defence sovereign capability, as identified by the Prime Minister in the Integrated Review statement to the House of Commons in November 2020.

Quantum physics describes how nature links the properties of isolated microscopic objects through interactions mediated by so-called quantum entanglement and that apply not just to atoms but also to particles of light, "photons". These discoveries led to the first "quantum revolution", delivering a range of transformative technologies such as the transistor and the laser that we now take for granted. We are now on the cusp of a second "quantum revolution", which will, over the next 5-10 years, yield a new generation of electronic and photonic devices that exploit quantum science. The challenge is to secure a leadership position in the race to the industrialisation of quantum physics to claim a large share of this emerging global market, which is expected to be worth £1 billion to the UK economy. QuantIC, the UK's centre for quantum imaging, was formed over four years ago to apply quantum technologies to the development of new cameras with unique imaging capabilities. Tangible impacts are the creation of 3 new companies (Sequestim, QLM and Raycal), technology translation into products through licencing (Timepix chip - Kromek) and the ongoing development with industry of a further 12 product prototypes. Moving forward, QuantIC will continue to drive paradigm-changing imaging systems such as the ability to see directly inside the human body, the ability to see through fog and smoke, to make microscopes with higher resolution and lower noise than classical physics allows and quantum radars that cannot be jammed or confused by other radars around them. These developments will be enabled by new technologies, such as single-photon cameras, detectors based on new materials and single-photon sensitivity in the mid-infrared spectral regions. Combined with our new computational methods, QuantIC will enable UK industry to lead the global imaging revolution. QuantIC will dovetail into other significant investments in the Quantum technology transfer ecosystem which is emerging in the UK. The University of Glasgow has allocated one floor of the £118M research hub to supporting fundamental research in quantum science and £28M towards the creation of the Clyde Waterfront Innovation Campus, a new £80M development in collaboration with Glasgow City Council and Scottish Enterprise focussing on the translation of nano and quantum science for enabling technologies such as photonics, optoelectronics and quantum. Heriot-Watt has invested over £2M in new quantum optics laboratories and is currently building a £20M Global Research Innovation and Discovery Centre opening in 2019 to drive the translation of emerging technologies. Bristol is creating a £43M Quantum Innovation centre which already has £21M of industrial investment. Strathclyde University is creating a second £150M Technology Innovation Centre around 6 priority areas, one of which is Quantum Technology. All of these form part of the wider UK Quantum Technology Programme which is set to transform the UK's world leading science into commercial reality in line with the UK's drive towards a high productivity and high-skill economy. QuantIC will lead the quantum imaging research agenda and act as the bond between parallel activities and investments, thus ensuring paradigm-changing innovation that will transform tomorrow's society.

The aim of this proposal is to create a robustly-validated virtual prediction tool called a "digital twin". This is urgently needed to overcome limitations in current industrial practice that increasingly rely on large computer-based models to make critical design and operational decisions for systems such as wind farms, nuclear power stations and aircraft. The digital twin is much more than just a numerical model: It is a "virtualised" proxy version of the physical system built from a fusion of data with models of differing fidelity, using novel techniques in uncertainty analysis, model reduction, and experimental validation. In this project, we will deliver the transformative new science required to generate digital twin technology for key sectors of UK industry: specifically power generation, automotive and aerospace. The results from the project will empower industry with the ability to create digital twins as predictive tools for real-world problems that (i) radically improve design methodology leading to significant cost savings, and (ii) transform uncertainty management of key industrial assets, enabling a step change reduction in the associated operation and management costs. Ultimately, we envisage that the scientific advancements proposed here will revolutionise the engineering design-to-decommission cycle for a wide range of engineering applications of value to the UK.

Persistent real-time, multi-sensor, multi-modal surveillance capabilities will be at the core of the future operating environment for the Ministry of Defence; such techniques will also be a core technology in modern society. In addition to traditional physics-based sensors, such as radar, sonar, and electro-optic, 'human sensors', e.g. from phones, analyst reports, social media, will provide new valuable signals and information that could advance situational awareness, information superiority, and autonomy. Transforming and processing this broad range of data into actionable information that meets these requirements presents many new challenges to existing sensor signal processing techniques. In a future where a large-scale deployment of multi-modal, multi-source sensors will be distributed across a range of environments, new signal processing techniques are required. It is therefore timely to consider the fundamental questions of scalability, adaptability, and resource management of multi-source data, when dealing with data that is high-volume, high-velocity, from non-traditional sources, and with high uncertainty. The UDRC Phase 3 project, Signal Processing in an Information Age is an ambitious initiative that brings together internationally leading experts from 5 leading centres for signal processing, data science and machine learning with 10 industry partners. Led by the Institute of Digital Communications at the University of Edinburgh, in collaboration with the School of Informatics at Edinburgh, Heriot-Watt University, University of Strathclyde and Queen's University Belfast. This multi-disciplinary consortium brings together unique expertise in sensing, processing and machine learning from across these research centres. The consortium has been involved in defence signal processing research through the UDRC phases 1 & 2, the MOD's Centre for Defence Enterprise, and the US Office of Naval Research. The team have significant experience in technology transfer, including: tracking and surveillance (Dstl), advanced radar processing (Leonardo, SEA); broadband beamforming (Thales); automotive Lidar and radar systems (ST Microelectronics, Jaguar Land Rover), and deep learning face recognition for security (AnyVision). This project will investigate fundamental mathematical signal and data processing techniques that will underpin future technologies required in the future operating environment. We will develop the underpinning inference algorithms to provide actionable information, that are computationally efficient, scalable, and multi-dimensional, and incorporate non-conventional and heterogeneous information sources. We will investigate multi-objective resource management of dynamic sensor networks that include both physical and human sensors. We will also use powerful machine learning techniques, including deep learning, to enable faster and robust learning of new tasks, anomalies, threats, and opportunities, relevant to operational security.