We have seen rapid development and growing interest in quantum technologies-based applications in the past decade and the overall global quantum technology market is expected to reach $31.57B by 2026. Most of these emerging quantum applications require single-photon avalanche diode (SPAD) detectors operating beyond the spectral range of silicon but with "silicon-like" performance. The use of "silicon-like" short-wave infrared (SWIR) SPAD detectors in the existing systems will immediately improve resolution and acquisition time for the existing imaging system and enhance the range and improve data rate for Quantum Key Distribution (QKD). However, the present commercially available InGaAs/InP based SPADs based on designs from more than two decades ago are unlikely to have a step change in their performance. Over the last five years, the advent of several innovations by way of novel III-V materials and semiconductor band structure engineering offers us the possibility of a paradigm shift in the performance of long wavelength detectors. The next revolution in the development of SPADs in the SWIR region will almost certainly be using novel materials and band structure engineered structures. Such a revolution will significantly enhance detection efficiency and fast timing. This new class of detectors will be evaluated on existing state-of-the-art testbeds for time-of-flight ranging/depth imaging and QKD. This Fellowship proposal has the ambition to sweep away the obstacles of material and processing problems that are hindering the development of affordable and easy operation SPADs, and to bridge gaps between material sciences, semiconductor physics, manufacturability and quantum technology applications in order to improve the scope and overall performance of next generation quantum technology-based applications.

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.

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

Modern manufacturing has been revolutionised by photonics. Lasers are central to this revolution, as they continue to transform the fast-changing manufacturing landscape. Photonics manufacturing represents an industry worth £10.5bn per annum to the UK economy, growing at about 8.5% annually and directly employing more than 70,000 people. UK Photonics exports are currently the 4th largest by value of any UK manufacturing sector, following automotive, aerospace and machinery exports. More importantly, UK Photonics exports more than 75% of its output relative to the UK manufacturing average of only 34%. Laser technology in particular underpins a number of leading UK industries in the aerospace, automotive, electronics, pharmaceuticals and healthcare engineering sectors. Over four decades, the Optoelectronics Research Centre at the University of Southampton has maintained a position at the forefront of photonics research. Its long and well-established track record in fibres, lasers, waveguides, devices, and optoelectronic materials has fostered innovation, enterprise, and cross-boundary multi-disciplinary activities. Advanced fibres and laser sub-systems, manufactured in Southampton by companies spun-out from the Optoelectronics Research Centre, are exported worldwide. Working closely with UK photonics industry, our interconnected and highly synergetic group will optimally combine different laser technologies into hybrid platforms for miniaturised, efficient, low-cost, agile and reconfigurable smart laser systems with software-driven performance. This is only possible because of the controllable, stable and robust, all-solid state nature of guided-wave lasers. A smart laser looks like its electronic equivalent - a single small sealed maintenance-free enclosure with a fully controlled output that is responsive to changes in the workpiece. The laser knows what material it is processing, how the process is developing and when it is finished. It is able to adapt to changes in the materials, their shape, reflectivity, thickness and orientation. This leads to new tools that enable innovative manufacturing processes that are critical in increasing competitiveness in important manufacturing sectors. Finally, the advanced laser technologies developed within this platform are expected to have a wider impact outside the manufacturing arena, in areas such as sensing, healthcare, and the medical sectors, as well as homeland security helping to establish an important laser sovereign capability.