In 1986, three physicists, Kardar, Parisi and Zhang, conjectured that all randomly evolving surfaces possessing three features, a smoothing mechanism, an underlying locally uncorrelated noise and a growth mechanism depending on the size of the slope, should have the same large-scale fluctuations, irrespective of their microscopic details. In other words, they predicted the existence of a Universality Class, that since then bares their name, and of a universal stochastic process, able to capture the behaviour of a wide class of models, such as turbulent liquid crystals, crystal growth on thin films, bacteria colony growth, etc. Over the last thirty years, their work stimulated the interest of a wide number of researchers, driven by the ambition to fully understand the nature of the KPZ Universality Class and to characterise this universal object. On the other hand, the Physics literature also predicts that, when a physical system possesses the same features apart from the slope dependence, then it belongs to a different Universality Class, the so-called Edwards-Wilkinson (EW) Universality Class, named after the two physicists that introduced it, and the universal process describing their behaviour is Gaussian and can be easily explicitly characterised. The first objective of this research proposal is to show that in the context of (1+1)-dimensional (one for time and one for space) randomly evolving interfaces, the classification given above is not exhaustive and another Universality Class needs to be considered. Our goal is to rigorously construct the universal object at its core, a stochastic process called Growing Brownian Castle, determine its characterising properties, give the first instances of its universality and analyse its relation with KPZ. In the context of the KPZ Universality Class, there is a model that plays a distinguished role and it is presumed to be universal itself. This model is a Stochastic Partial Differential Equation (SPDE), the KPZ Equation. Despite its importance, a satisfactory solution theory for this equation in one spatial dimension was established only recently thanks to the theory of Regularity Structures, by M. Hairer. The techniques that are now available allow for a systematic study of its universality and this research program intends to establish it for a family of models driven by conservative dynamic, which has never been considered so far. For evolving surfaces in (1+2)-dimensions, the Universality Classes picture is subtler because the slope can evolve in different directions that could compete with each other. This proposal focuses on the case in which the contribution of the slope sizes in the different directions averages out. This class of models is called Anisotropic KPZ Universality Class and the long-standing conjecture, coming from the Physics literature, is that this class is nothing but EW in dimension 2. In other words it is expected that the slope does not play any role at all. The project aims at showing such a result for the Anisotropic KPZ Equation, a singular SPDE that cannot be treated by the theory of Regularity Structures mentioned above and for which radically new ideas are needed. At last, the random operator we will focus on is the Anderson-Hamiltonian. Its importance lies on the fact that it is connected with the parabolic Anderson model, the scaling limit of random motion in random potential or branching processes in random media, and many others. We will determine some of its properties that will shed some light on its universal nature.

The project brings together academics, performers, sound curators and archivists, and sound engineers and technicians to engage in discussion concerning the use and status of early recordings (1890-1945) as sources for the study of performance practice and performance history, establishing foundations for further collaborative research and knowledge exchange in the area. Researchers and performers have been using early recordings as primary sources for the study of performance and music history for the last thirty years, in topics ranging from the minutiae of performance practice in specific styles and instruments, to the radical transformations that early recording technologies introduced in listening practices and in discourses around music and performance. During this period, technological advances have made early recordings more widely accessible, with collections and archives around the world digitizing their holdings and making them available online for free or at negligible cost. However, most such research activity has been conducted in relative isolation, and opportunities for researchers to engage in discussion about their work with an audience of their peers are few and far between. This lack of connectedness has prevented the field from tackling ambitious, comparative research questions centring around systematic historical change, and detracted from its relevance and visibility both in the broader field of musicology and among non-academic performers and general concert audiences. The project proposes to tackle these issues through the following interconnected collaborative activities: -Five symposia (4 in different cities across the UK, 1 hosted by partner TU Berlin) will provide opportunities for experts (musicologists, performers, sound curators, archivists and engineers engaged in sound curation and digitizing initiatives - both based in and outside HEI) to engage in methodological discussion with the aim of both co-creating collaborative resources and identifying collaborative research and knowledge exchange opportunities in the field. -A concert series attached to the symposia will allow audiences across the UK to familiarize themselves with practice-led research conducted by network members, while allowing the latter to reflect, in conversation with other network members, on good practice, opportunities and challenges for knowledge exchange. -A series of video interviews with network members filmed at the symposia and concert series will make accessible an array of approaches to early recordings to other HEI and non-HEI experts, as well as to musicologists, performers and performance students beyond the immediate area of study. These videos will be accompanied by an open-access handbook for similar audiences, expanding on the issues raised in the interviews (to be published after the grant period). The project will also establish a permanent forum for those interested in early recordings as sources for the study of performance practice and history. This open international research network will organize regular conferences and meetings, fostering collaborative activities between its members. The forum's establishment will be supported by an 'early recording roadmap', drafted collaboratively by network-members, identifying urgent research questions and flagging up potential areas for knowledge exchange collaborations.

Higher living standards and a growing world population are the drivers behind continuous increases in greenhouse gas emission and industrial energy use. This provides growing pressure on chemical industries to develop more sustainable and efficient chemical transformations based on innovative new technologies. Light-driven plasmonic catalysis offers a promising route to more sustainable and energy efficient chemical transformations than conventional industrial-scale catalysis by replacing petrochemical reactants and energy sources with abundant feedstocks such as carbon dioxide from the atmosphere and renewable energy from sunlight. In addition, light energy can selectively be transferred via excited electrons in metal nanoparticles, so-called "hot" electrons, to molecules and enables more specific chemical reactions than conventional catalysis, potentially increasing yield and decreasing unwanted side products. Underlying this unconventional form of chemistry is the intricate coupling of light, hot electrons, and reactant molecules, the lack of understanding of which has inhibited systematic design and study of reaction parameters such as particle size, shape, and optimal light exposure. A predictive theory of hot-electron chemistry will support the adaptation of this technology in the chemical industry, which holds the potential to significantly reduce the industry's carbon footprint. The aim of this project is to develop and exploit a computational simulation framework to understand, predict, and design light-driven chemical reactions on light-sensitive metallic nanoparticles and surfaces, so-called plasmonic nanocatalysts. The vision behind this fellowship is to provide quantum theoretical methods that fill a conceptual and methodological gap by providing accurate and feasible computational prediction of experimentally measurable chemical reaction rates as a function of catalyst design parameters relevant to the real-world application of this technology. In synergy with experimental project partners, the fellow will lead a research team of 2 postdoctoral researchers to develop highly efficient computational chemistry methodology, which will be applied to scrutinize mechanistic proposals, support and guide experimental efforts on light-driven plasmonic carbon dioxide reduction chemistry, and to construct reaction rate models relevant to improve the industrial viability of this technology. The aim is to provide a step-change in the mechanistic understanding of light-driven plasmonic reduction catalysis on the example of carbon monoxide and carbon dioxide transformation to enable rational design of catalyst materials with wide implications for continuous photochemistry and electrochemistry applications in industry. These applications will be explored by continuous engagement efforts of the fellow with leading chemical and petrochemical companies. With this project, the fellow will establish an international track record by fostering existing and establishing new collaborations with the goal to become a recognized researcher in this comparably young field.

In today's society, the massive deployment of smart devices, the popularity of online services and social networks, and the increasing global data traffic, makes the ability to process large data volumes absolutely crucial. Demand for Artificial Intelligence (AI) has therefore exploded, fuelled by an increasing number of industries (e.g. energy, finance, healthcare, defence) heavily relying on the efficient processing of large data sets. Nonetheless, the ever-growing data processing demand creates a pressing need to find new paradigms in AI going beyond current systems, capable of operating at very high speeds whilst retaining low energy consumption. The human brain is exceptional at performing very quickly, and efficiently, highly complex computing tasks such as recognising patterns, faces in images or a specific song from just a few sounds. As a result, computing approaches inspired by the powerful capabilities of networks of neurons in the brain are the subject of increasing research interest world-wide, and are in fact already used by current AI platforms to perform these (and other) complex functions. Whilst these brain-inspired artificial neural networks (ANNs) are supported to date by traditional micro-electronic technologies, photonic techniques for brain emulation have also recently started to emerge due to their unique and superior properties. These include very high speeds and reduced interference, among others. Remarkably, ubiquitous photonic devices such as vertical-cavity surface emitting lasers (VCSELs), the very same devices used in supermarket barcode scanners, computer mice and in mobile phones for auto-focus functionalities, can exhibit responses analogous to those of neurons but up to 1 billion times faster. VCSELs are also compact, inexpensive and allow practical routes for integration in chip modules with very low footprints (just a few mm2) making them ideal for the development of ultrafast photonic ANNs using ultrafast light signals instead of electric currents to operate. This permits exploring radically new research directions aiming at exploiting the full potential of light-enabled technologies for new paradigms in ultrafast AI. This Fellowship project will focus on this key challenge to develop transformative photonic ANNs using VCSELs as building blocks capable of performing complex computational tasks at ultrafast speeds, using data rates below 1 billionth of a second to operate. These will include the ultrafast prediction of complex data signals, of interest for example in meteorology forecasting, to very high speed data classification of interest in green-energy systems (e.g. analysis of wind patterns in off-shore wind-energy farms). The research milestones of this programme are: (1) the design and fabrication of photonic ANNs using coupled VCSELs as building blocks, emulating the operation of the human brain at ultrafast speeds; (2) the development of chip-scale modules of VCSEL based photonic ANNs; (3) the demonstration of complex data processing tasks with photonic ANNs at ultrafast speeds (at data rates below 1 billionth of a second); (4) the delivery of photonic systems for AI, tackling key functionalities across strategic UK economic sectors (e.g. energy, defence). In summary, by bringing together the hitherto disparate fields of brain-inspired computing and photonics, this programme proposes unique pioneering research in photonic ANNs for future ultrafast AI technologies.

The interaction between nano-objects of different dimensionality, e.g. electrostatic Coulomb-coupling of a zero-dimensional quantum dot (QD) to a two-dimensional (2D) system is of fundamental interest and of great relevance for charge-based memories. This interaction between a single QD and a 2D system shall be studied here. Innovative use of the complementary expertise of the partners will combine, for the first time, Sb-based QDs with a split-gate structure, which will allow the precise control of the charge-state of a single QD. Sb-based QDs have strong hole confinement yielding a potential retention time of many years at room temperature, enabling the analysis of the influence of charged QDs on a 2D system up to 300 K. In the mid-long term perspective, the results could be important for future generations of memories: knowledge of the interaction of a 2D system with a single QD might allow us to reach the ultimate limits of charged-based memories (e.g. Flash).