We seek to exploit the highly advantageous properties of III-V semiconductors to achieve agenda setting advances in the quantum science and technology of solid state materials. We work in the regime of next generation quantum effects such as superposition and entanglement, where III-V systems have many favourable attributes, including strong interaction with light, picosecond control times, and microsecond coherence times before the electron wavefunction is disturbed by the environment. We employ the principles of nano-photonic design to access new regimes of physics and potential long term applications. Many of these opportunities have only opened up in the last few years, due to conceptual and fabrication advances. The conceptual advances include the realisation that quantum emitters emit only in one direction if precisely positioned in an optical field, that wavepackets which propagate without scattering may be achieved by specific design of lattices, and that non-linearities are achievable at the level of one photon and that quantum blockade can be realised where one particle blocks the passage of a second. The time is now right to exploit these conceptual advances. We combine this with fabrication advances which allow for example reconfigurable devices to be realised, with on-chip control of electronic and photonic properties. We take advantage of the highly developed III-V fabrication technology, which underpins most present day solid-state light emitters, to achieve a variety of chip-based quantum physics and device demonstrations. Our headline goals include reconfigurable devices at the single photon level, a single photon logic gate based on the fully confined states in quantum dots positioned precisely in nano-photonic structures, and coupling of states by designed optical fields, taking advantage of the reconfigurable capability, to enhance or suppress optical processes. Quantum dots also have favourable spin (magnetic moments associated with electrons) properties. We plan to achieve spins connected together by photons in an on-chip geometry, a route towards a quantum network, and long term quantum computer applications. As well as quantum dots, III-V quantum wells interact strongly with light to form new particles termed polaritons. We propose to open the new field of topological polaritonics, where the nano-photonic design of lattices leads to states which are protected from scattering and where artificial magnetic fields are generated. This opens the way to new coupled states of matter which mimic the quantised Hall effects, but in a system with fundamentally different wavefunctions from electrons. Finally our programme also depends on excellent crystal growth. We target one of the main issues limiting long term scale up of quantum dot technologies, namely site control. We will employ two approaches, which involve a combination of patterning, cleaning and crystal growth to define precisely the quantum dot location, both based around the formation of pits to seed growth in predetermined locations. Success here will be a major step in bringing semiconductor quantum optics into line with the position enjoyed by the majority of established semiconductor technologies where scalable lithographic processes have been a defining feature of their impact.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Sustainable development goals are formulated within European policies aiming at a sustainable, and yet competitive, circular economy. Most recently, in the Communication on the European Green Deal, the European Commission committed to the adoption of a new Circular Economy Action Plan to accelerate and continue the transition towards a circular economy. This commitment is heavily reflected in novel approaches to materials science, production, and development, and it is driven by technologies such as emerging additive manufacturing. There are two critical fronts in which the development of new materials' technologies will play a decisive role in shaping this much needed green transition; the development of advanced materials whose manufacturability strikes an optimal balance between high demands for functionality, and the improvement of sustainability in the production chain. Focus on the functionality in its own right, e.g., in materials for the transport, construction and energy generation industries, together with unprecedented need for better, cheaper, and sustainable materials is timely. However, maintaining or further increasing the performance and reliability of existing materials, including lightweight composite materials, metal alloys or ceramics, presents challenges at the heart of this current paradigm. For many applications joining similar or dissimilar materials is indispensable, while adhesive bonding is an attractive alternative to classical joining methods, leading to new composites with more functions, and better mechanical and physical performance. Bonding processes and adhesives are used in a wide range of engineering structures for primary and secondary bonding. However, certain unresolved questions remain open regarding the exploitation of the potential of adhesive bondlines to truly contribute to the development of sustainable engineering structures-those promoting prevention, reuse and repurposing of structural components. This research project proposes to deal with these unresolved questions and to recommend innovative material and structural concepts, new modelling techniques, and novel experimental methods for sustainable structural bonding. The project has multiple objectives, necessitating an interdisciplinary approach for addressing each one of them. The main goal is to exploit the concept of Mechanical Metamaterials within the framework of adhesive bonding and to develop solutions for architected and tuneable bondlines, namely "meta-adhesives". The "meta-adhesive" concept will be investigated by implementing well designed metamaterial lattices into the adhesive bulk. The investigation will span in several scale levels, from the material to the structural component and will respond to specific basic research questions such as: (i) whether the metamaterial scaling properties affect the bondline fracture toughness in a predictable manner; (ii) how the features of failure (from geometrical to material failure) will be affected and how load transfer will be influenced by having architected bondlines; (iii) in which manner and to what extent can the meta-adhesive bondline be functionalised in order to serve the aforementioned prevention/reuse and repurposing for improved sustainability in structures. This fellowship will allow the applicant to use and expand his knowledge in the field of Mechanical Metamaterials to deal with unresolved questions and to recommend innovative material and structural concepts, new modelling techniques, and novel experimental methods for sustainable structural bonding.

The project "Compressive Imaging in Radio Interferometry" (CIRI) aims to bring new advances for interferometric imaging with next-generation radio telescopes, together with theoretical and algorithmic evolutions in generic compressive imaging. Radio Interferometry (RI) allows observations of the sky at otherwise inaccessible angular resolutions and sensitivities, providing unique information for astrophysics and cosmology. New telescopes are being designed, such as the Square Kilometer Array (SKA), whose science goals range from astrobiology and strong field gravity, to the probe of early epochs in the Universe when the first stars formed. These instruments will target orders of magnitudes of improvement in resolution and sensitivity. In this context, they will have to cope with extremely large data sets. Associated imaging techniques thus literally need to be re-invented over the next few years. The emerging theory of compressive sampling (CS) represents a significant evolution in sampling theory. It demonstrates that signals with sparse representations may be recovered from sub-Nyquist sampling through adequate iterative algorithms. CIRI will build on the theoretical and algorithmic versatility of CS and leverage new advanced sparsity and sampling concepts to define, from acquisition to reconstruction, next-generation CS techniques for ultra-high resolution wide-band RI imaging and calibration techniques. The new techniques, and the associated fast algorithms capable of handling extremely large data sets on multi-core computing architectures, will be validated on simulated and real data. Astronomical imaging is not only a target, but also an essential means to trigger novel generic developments in signal processing. CIRI indeed aims to provide significant advances for compressive imaging thereby reinforcing the CS revolution, which finds applications all over science and technology, in particular in biomedical imaging. CIRI is thus expected to impact science, economy, and society by developing new imaging technologies essential to support forthcoming challenges in astronomy, and by delivering a new class of compressive imaging algorithms that can in turn be transferred to many applications, starting with biomedical imaging.

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.