Battery electrified power is predicted to become the dominant mode of propulsion in future light duty transport. For sustainable heavy duty applications challenges remain around practical range, payload and total cost. Currently there is no economically viable single solution. For commercial marine vessels the problem is compounded by long service lives, with bulk carriers, tankers and container ships the main contributors to greenhouse gases. Ammonia (NH3) has excellent potential to play a significant role as a sustainable future fuel in both retrofitted and advanced engines. However, significant uncertainties remain around safe and effective end use, with these unknowns spanning across fundamental understanding, effective application and acceptance. This multi-disciplinary programme seeks to overcome the key related technical, economic and social unknowns through flexible, multidisciplinary research set around disruptive NH3 engine concepts capable of high thermal efficiency and ultra low NOx. The goal is to accelerate understanding, technologies and ultimately policies which are appropriately scaled and "right first time".

Research Area: ART-AI is a multidisciplinary CDT, bringing together computer science, social science and engineering so that its graduates will be specialists in one subject, but have substantial training and experience in the others. The ART-AI management team brings together research in AI, HCI,politics/economics, and engineering, while the CDT as a whole has a team of >40 supervisors across seven departments in three faculties and the institutes for policy research (IPR) and for mathematical innovation (IMI). This is not a marriage of convenience: many CDT members have experience of interdisciplinary working and together with CDT cohorts and partners, we will create accessible, transparent and intelligible AI, driven by ethical and responsible principles, to address issues in, for example, policy design and political decision-making, development of trust in AI for humans and organisations, autonomous systems, sensing and data analysis, explanation of machine decision-making, public service design, social simulation and the ethics of socio-technical systems. Need: Hardly a day passes without a news article on the wonders and dangers of AI. But decisions - by individuals, organisations, society and government - on how to use or not use AI should be informed and ethical. We need policy experts to recognise both opportunities and threats, engineers to extend our technical capabilities, and scientists to establish what is tractable and to predict likely outcomes of policies and innovations. We need mutually informed decisions taking account of diverse needs and perspectives. This need is expressed in measured terms by a slew of major reports (see Case for Support) and Commons and Lords committees, all reflecting the UKCES Sector Insights (Evidence report #92, 2015) prediction of a need by 2022 for >0.5M additional workers in the digital sector against just a third of that number graduating annually. To realise the government vision for AI (White Paper), a critical fraction of those 0.5M workers need to be leaders and innovators with in-depth scientific and technical knowledge to make the right calls on what is possible, what is desirable, and how it can be most safely deployed. Beyond the UK, a 2018 PwC report indicates AI will impact ~10% of jobs, or ~326 million globally by 2030, with ~33% in high-skill jobs across most economic sectors. The clear conclusion is a need for a significant cadre of high-skill workers and leaders with a detailed knowledge of AI, an understanding of how to utilise it, and its political, social and economic implications. The ART-AI is designed to deliver these in collaboration and co-creation with stakeholders in these areas. Approach: ART-AI will produce interdisciplinary graduates and interdisciplinary research by (i) exposing its students to all three disciplines in the taught elements, (ii) fostering development of multi-discipline perspectives throughout the doctoral research process, and (iii) establishing international and stakeholder perspectives whilst contributing to immediate, real-world problems through a programme of visiting lecturers, research visits to leading institutions and internships. The CDT will use some conventional teaching, but the innovations in doctoral training are: (i) multi-disciplinary team projects; (ii) structured and facilitated horizontal (intra-cohort) peer learning and vertical (inter-cohort) mentoring, and in the interdisciplinary cross-cohort activities in years 2-4; (iii) demonstrated contextualisation of the primary discipline research in the other disciplines both at transfer (confirmation) at the end of year 2 and in the final dissertation. Each student will have a primary supervisor from their main discipline, a co-supervisor from at least one of the other two, and where appropriate, one from a CDT partner, reflecting the interdisciplinarity and co-creation that underpin the CDT.

Intelligent Visual and Interactive Technology allows us to perceive, understand and re-create the world around us. With it we can digitise the world with 3D cameras, use Artificial Intelligence (AI) to predict and enhance the health of people within our world or to educate and train them. It allows us to experience this world, or imagined ones, through immersive technologies, movies and video games, and interact with these worlds through technologies that analyse our movement and behaviour. There is a clear benefit to applying this technology across domains, for specific health or education purposes, but doing so requires coordinated action and genuine democratisation of the underpinning technologies, such that non-expert users are empowered. To address this challenge, CAMERA 2.0 will perform world-leading research in Intelligent Visual and Interactive Technology - underpinned by academic and partner expertise across Computer Vision, Computer Graphics, Human Computer Interaction (HCI) and AI - and engage a range of partners to generate impact and translate this technology across a range of themes. This multi-disciplinary approach is supported by academic and external partner expertise spanning healthcare, biomechanics, sports performance and psychology. These collaborations will allow us to carry out new research, create new impacts and develop further partnerships that would otherwise be impossible to achieve. This proposal builds on our highly successful Next Stage Digital Economy Centre for the Analysis of Motion Entertainment Research and Applications (CAMERA). Over the last 4 years, we have built a team of 14 academics and over 40 PhDs and researchers who have created real impact, alongside our partners, across themes of i) Entertainment; ii) Health, Rehabilitation and Assistive Technologies and; iii) Human Performance Enhancement. CAMERA 2.0 will also focus on three themes, supported by over 20 impact partners: i) Creative Science and Technology, ii) Digital Health and Assistive Technology and iii) Human Performance Enhancement. Furthermore, CAMERA 2.0 will work closely with our EPSRC CDT in Digital Entertainment and our new UKRI CDT in Accountable, Responsible and Transparent AI (ART-AI). Our research programme will deliver continuing impact through four primary mechanisms: (i) Theme Driven Impact Projects, (ii) Cross-Cutting Theme R&D Challenges, (iii) Reactive Impact Projects and (iv) Open Community Engagement. Theme Driven Impact Projects will be 12 to 24-month projects co-designed through sand-pits and co-delivered with partners. Although primarily aligned with a single theme they will overlap with at least one other. Our Cross-Cutting Theme R&D Challenges engage with R&D challenges shared by partners/academics across themes. Translating innovations across themes not only democratises and accelerates technology adoption but can significantly enhance impact. This will be addressed through key research projects, that support and feed into all other activities. Our reactive model allows us to carry out commercial projects as research impact vehicles at short notice - essential being able to work with the short-deadline driven creative sector. CAMERA 2.0 evolves our unique reactive impact model by placing our CAMERA student technical team at its core under the supervision of our experienced studio managers. Impact through Open Engagement. Our ambition is to raise the level of UK and international DE research through collaboration and technology democratisation. CAMERA 2.0 will operate an open-door model for reasonable access to facilities, data, software and training. In coordination with commitments from the University of Bath and external EU funding we are expanding our physical facilities and technical team to provide assisted motion capture and immersive technology training for free to over 100 creative industries, HEIs and healthcare companies.

The overarching aim is to develop a facility for the testing and evaluating of large structures, called Structure 2025. To construct such a facility it is necessary to purchase specialist equipment, which comprises imaging, loading and control systems. Structures 2025 will provide a novel integrated imaging and loading system that is flexible, and can be used for the testing and assessment of a wide range of structures across industry sectors. The unique feature of Structures 2025 is that it will, for the first time, enable data-rich studies of the behaviour of large components and structures subjected to realistic loading scenarios mimicking the behaviour of a structure in service. It will be possible to model the loads felt by aircraft in flight, railway structures, bridges and cars and understand better how the structure supports the load experienced in service. Structures 2025 will enable the introduction of new lightweight materials into transport systems allowing energy savings and a more sustainable approach to design. The uniqueness of Structures 2025 is predicated on imaging, where large amounts of data can be collected to provide information about the structural response. The imaging will be based on both visible light and infra-red camera systems which capture data from the loaded structure and used to evaluate strains and deformations. Traditional sensors take only point readings, whereas images provide data over a wide field of view, since each sensor in the imaging device provides a measurement, the terminology 'data-rich' is applied. A complete system integration will be developed and implemented, that combines the load application using a multi-actuator loading system with the imaging systems. The combination of techniques into a single integrated system will be unique internationally, and will enable the accurate assessment of the interactions between material failure mechanisms/modes and structural stiffness/strength driven failure modes on a hitherto unattainable level of physical realism. Structures 2025 will provide what can be termed high-fidelity data-rich testing of structural components, to integrate with multi-scale computational modelling to provide better predicitive models of structural failure and create safer and more efficient structures. Structures 2025 will be developed in close collaboration with 16 industry partners, representing the rail infrastructure, civil engineering, experimental technique development, energy systems, marine and offshore, and aerospace sectors, as well as several university partners.