This proposal aims to develop a new generation of sensor system for 3D vision in automotive and industrial applications, based on single photon avalanche diodes (SPADs). A key advantage of SPADs is that they enable the construction of accurate 3D images via time-of-flight, that is by emitting laser pulses towards a target, and timing the return of the reflected signal. Crucially, SPADs are able to detect targets from only the few photons (particles of light) that can be acquired in the millisecond timescales critical for the detection and tracking of fast moving objects. The proposed sensor will feature complex on-chip processing to extract salient features at frame rates an order-of-magnitude faster than current (2D) cameras in advanced driver-assistance systems, and thus provide crucial advantages in collision avoidance.

Machine Learning (ML) already has a dramatic impact on our daily lives. ML developments in large language models and deep generative models cement that further. The recent explosion in ML, however, is built on the back of improved computer systems able to train and generate ever more powerful models. Systems design fundamentally defines ML performance and capability. This is true for Internet-scale ML and artificial intelligence (AI). Yet, more recently, it is especially evident in distributed, efficient, device-oriented, secure, personalised, privacy-preserving ML. UK strength in this fast developing area is dependent on a skilled R\&D workforce. Systems research and ML research are symbiotic. Current innovation in systems research is driven by the ubiquitous need for efficient and reliable ML. ML research, conversely, is steered by deployment capability and the economic and environmental impact of the resulting systems. Furthermore, systems research increasingly relies on ML methods to automate design, and ML research develops such methods. Major gains are made when the development of ML and systems are co-developed and co-optimized. This is relevant across a broad spectrum of industries: in-car systems, medical devices, mobile phones, sensor networks, condition monitoring systems, high-performance compute and high-frequency trading. Yet PhD training that brings together systems and ML is rare; research training is often siloed in the individual sub-disciplines. Instead, we need researchers trained in both fields and experienced in working across them. Hence: The ML Systems CDT will train a new type of student -- the ML-systems researcher. The ML Systems researcher is critically capable in both fields, and has collaborative research experience across the systems-ML stack. An example concretises this. A company is developing and deploying wearable body monitors. Effective models must be learnt on collected data, but data must be privacy preserving and bandwidth minimized. This is then personalised to each individual, adaptable to circumstance while being battery efficient and not connection dependent. To manage such a project requires knowledge of effective data-efficient ML signal analysis methods, designed and optimized for low-power hardware, itself tailored for the purpose through ML optimization methods. Knowledge of personalisation methods and the payoffs of privacy preserving methods vitally complement this. The societal impact, e.g.\ on those who might be obsessive about their medical state must also be considered, and will impact development. This CDT will train individuals with cross-cutting capability in all these components. Students must have broad understanding of different hardware designs, different platforms, different environments, different models, and different goals beyond their immediate research focus. This makes a cohort-based CDT vital. Standard PhD training in ML systems can result in research focus on a single ML technique and a single system. The CDT treats ML Systems as a holistic discipline. Cohort interaction, and integration gives students real experience across multiple systems, approaches and methodologies. Furthermore students will join together to contribute to a unified toolkit for the ML-Systems stack, and make use of others' contributions to that toolkit. On leaving the CDT, our graduates will understand fully where to focus resources to best improve a company's real-world ML development - whether that be at the ML-algorithm level, the hardware level, the compiler, level or even the legal level. They will be able to evaluate work at every level. We expect our graduates to be the leading team managers in real-world cutting-edge company ML.

Quantum Technology is based on quantum phenomena that govern physics on an atomic scale, enabling key breakthroughs that enhance the performance of classical devices and allow for entirely new applications in communications technology, imaging and sensing, and computation. Quantum networks will provide secure communication on a global scale, quantum sensors will revolutionise measurements in fields such as geology and biomedical imaging, and quantum computers will efficiently solve problems that are intractable even on the best future supercomputers. The economic and societal benefit will be decisive, impacting a wide range of industries and markets, including engineering, medicine, finance, defence, aerospace, energy and transport. Consequently, Quantum Technologies are being prioritised worldwide through large-scale national or trans-national initiatives, and a healthy national industrial Quantum Technology ecosystem has emerged including supply chain, business start-ups, and commercial end users. Our Centre for Doctoral Training in Applied Quantum Technologies (CDT-AQT) will address the national need to train cohorts of future quantum scientists and engineers for this emerging industry. The training program is a partnership between the Universities of Strathclyde, Glasgow and Heriot-Watt. In collaboration with more than 30 UK industry partners, CDT-AQT will offer advanced training in broad aspects of Quantum Technology, from technical underpinnings to applications in the three key areas of Quantum Measurement and Sensing, Quantum Computing and Simulation, and Quantum Communications. Our programme is designed to create a diverse community of responsible future leaders who will tackle scientific and engineering challenges in the emerging industrial landscape, bring innovative ideas to market, and work towards securing the UK's competitiveness in one of the most advanced and promising areas of the high-tech industry. The quality of our training provision is ensured by our supervisors' world-class research backgrounds, well-resourced research environments at the host institutions, and access to national strategic facilities. Industry engagement in co-creation and co-supervision is seen as crucial in equipping our students with the transferable skills needed to translate fundamental quantum physics into practical quantum technologies for research, industry, and society. To benefit the wider community immediately, we will make Quantum Technologies accessible to the general public through dedicated outreach activities, in which our students will showcase their research and exhibit at University Open Days, schools, science centres and science festivals.

The Hub will address two challenges introduced by the use of Edge Computing (EC) to support emerging AI algorithms: dealing with cyber disturbances and managing data quality. The Hub will achieve these through a unique 3x3x3x2 matrix that reflects the complexity of these systems: (1) 3 real-world application domains (2) 3 tiers of EC architecture (3) 3 ground-breaking research work streams (4) 2 industry engagement work streams Their inter-relationships will be examined by a multi-disciplinary team with track records in EC architecture (Newcastle, Cardiff, St. Andrews, UWS, Imperial, Hull), foundational AI and Data Quality (Southampton, Durham, QUB, Swansea), wireless communication (Cardiff, UWS), device malfunction, attack detection and prevention (Newcastle, Lancaster, Cardiff, Warwick), and AI security (Lancaster, Swansea, Durham, Warwick). This network will enable us to engage with regional development agencies in these areas. Applications include autonomous electric vehicles, energy security and remote healthcare. At the Newcastle Urban Observatory test-bed, a world-leading UK-funded effort collaborating with sensor system manufacturers, software companies and others, we will use interactions across 3 tiers of EC architecture: sensors (Tier 1), edge devices that control them (Tier 2), and cloud-based data storage and processing (Tier 3), to identify the benefit of these interactions in the real-world data processing. The agenda will be underpinned by activities in 5 interrelated work streams. We have strategies in: Embedding Equality, Diversity & Inclusion: We are committed to EDI policies of UKRI and EPSRC Councils and EDI policies of our members. From these, we will form our guiding principle around EDI for members to adhere to in all matters related to the Hub, including recruitment, research, workshops, project allocations, outreach activities, etc. All core committees will have an EDI champion. We will ensure that activities are fair, free from bias and preference of any kind, and uphold the respect and integrity of all members. The Hub is constituted of members from diverse ethnic backgrounds, races, and gender and has intrinsically diverse and multicultural characteristics. We will actively encourage students from under-represented groups to pursue industry-funded PhDs with the Hub. The PDRA requirement in the Hub will, while maintaining the best talent, offer equal opportunity to candidates of all backgrounds, disabilities, sexual orientations, gender, and ethnicity. The Hub will use institutional infrastructure to support the well-being of staff and members. Intellectual Property Management: While most research outcomes will be made public (e.g., software open access), some may be subject to patents. Participating universities have commercialisation offices to identify, assess, protect, manage and commercially develop IP to maximise national benefits from public investment in research, which we will use to commercialise significant outcomes. Information about services, standards used, and other technical details will be made public to attract industrial partners and to promote training in the new technologies. Non-technical press releases and notes will be available to general audiences. An in-principle agreement has been reached with consortium members that each shall retain ownership of any background IP contributed to the project and that the ownership of project-generated IP shall be shared based on respective partner contributions. Hub activities will in general follow the National Principles of Intellectual Property Management for Publicly Funded Research and this will be applied to each project managed under the feasibility fund. Non-Disclosure Agreements with commercial partners will be in place to manage sensitive information. Specific terms regarding IP will be further defined in a collaborative research agreement before the commencement of the project.

In a consortium led by Heriot-Watt with St Andrews, Glasgow, Strathclyde, Edinburgh, Dundee, Huddersfield and NPL, the "EPSRC CDT in Use-Inspired Photonic Sensing and Metrology" responds to the focus area of "Meeting a User-Need and/or Supporting Civic Priorities" and aligns to EPSRC's Frontiers in Engineering & Technology priority and its aim to produce "tools and technologies that form the foundation of future UK prosperity". Our theme recognises the key role that photonic sensing and metrology has in addressing 21st century challenges in transport (LiDAR), energy (wind-turbine monitoring), manufacturing (precision measurement), medicine (disease sensors), agri-food (spectroscopy), security (chemical sensing) and net-zero (hydrocarbon and H2 metrology). Building on the success of our earlier centres, the addition of NPL and Huddersfield to our team reflects their international leadership in optical metrology and creates a consortium whose REF standing, UKRI income and industrial connectivity makes us uniquely able to deliver this CDT. Photonics contributes £15.2bn annually to the UK economy and employs 80,000 people--equal to automotive production and 3x more than pharmaceutical manufacturing. By 2035, more than 60% of the UK economy will rely on photonics to stay competitive. UK companies addressing the photonic sensing and metrology market are therefore vital to our economy but are threatened by a lack of doctoral-level researchers with a breadth of knowledge and understanding of photonic sensing and metrology, coupled with high-level business, management and communication skills. By ensuring a supply of these individuals, our CDT will consolidate the UK industrial knowledge base, driving this high-growth, export-led sector whose products and services have far-reaching impacts on our society. The proposed CDT will train 55 students. These will comprise at least 40 EngD students, characterised by a research project originated by a company and hosted on their site. A complementary stream of up to 15 PhD students will pursue industrially relevant research in university labs, with more flexibility and technical risk than in an EngD project. In preparing this bid, we invited companies to indicate their support, resulting in £5.5M cash commitments for 102 new students, considerably exceeding our target of 55 students, and highlighting industry's appetite for a CDT in photonic sensing and metrology. Our request to EPSRC for £6.13M will support 35 students, with the remaining students funded by industrial (£2.43M) and university (£1.02M) cash contributions, translating to an exceptional 56% cash leverage of studentship costs. The university partners provide 166 named supervisors, giving the flexibility to identify the most appropriate expertise for industry-led EngD projects. These academics' links to >120 named companies also ensure that the networks exist to co-create university-led PhD projects with industry partners. Our team combines established researchers with considerable supervisory experience (>50 full professors) with many dynamic early-career researchers, including a number of prestigious research fellowship holders. A 9-month frontloaded residential phase in St Andrews and Edinburgh will ensure the cohort gels strongly, equipping students with the knowledge and skills they need before starting their research projects. These core taught courses, augmented with electives from the other universities, will total 120 credits and will be supplemented by accredited MBA courses and training in outreach, IP, communication skills, RRI, EDI, sustainability and trusted-research. Collectively, these training episodes will bring students back to Heriot-Watt a few times each year, consolidating their intra- and inter-cohort networks. Governance will follow our current model, with a mixed academic-industry Management Committee and an International Advisory Committee of world-leading experts.