The CDT proposal 'Fuel Cells and their Fuels - Clean Power for the 21st Century' is a focused and structured programme to train >52 students within 9 years in basic principles of the subject and guide them in conducting their PhD theses. This initiative answers the need for developing the human resources well before the demand for trained and experienced engineering and scientific staff begins to strongly increase towards the end of this decade. Market introduction of fuel cell products is expected from 2015 and the requirement for effort in developing robust and cost effective products will grow in parallel with market entry. The consortium consists of the Universities of Birmingham (lead), Nottingham, Loughborough, Imperial College and University College of London. Ulster University is added as a partner in developing teaching modules. The six Centre directors and the 60+ supervisor group have an excellent background of scientific and teaching expertise and are well established in national and international projects and Fuel Cell, Hydrogen and other fuel processing research and development. The Centre programme consists of seven compulsory taught modules worth 70 credit points, covering the four basic introduction modules to Fuel Cell and Hydrogen technologies and one on Safety issues, plus two business-oriented modules which were designed according to suggestions from industry partners. Further - optional - modules worth 50 credits cover the more specialised aspects of Fuel Cell and fuel processing technologies, but also include socio-economic topics and further modules on business skills that are invaluable in preparing students for their careers in industry. The programme covers the following topics out of which the individual students will select their area of specialisation: - electrochemistry, modelling, catalysis; - materials and components for low temperature fuel cells (PEFC, 80 and 120 -130 degC), and for high temperature fuel cells (SOFC) operating at 500 to 800 degC; - design, components, optimisation and control for low and high temperature fuel cell systems; including direct use of hydrocarbons in fuel cells, fuel processing and handling of fuel impurities; integration of hydrogen systems including hybrid fuel-cell-battery and gas turbine systems; optimisation, control design and modelling; integration of renewable energies into energy systems using hydrogen as a stabilising vector; - hydrogen production from fossil fuels and carbon-neutral feedstock, biological processes, and by photochemistry; hydrogen storage, and purification; development of low and high temperature electrolysers; - analysis of degradation phenomena at various scales (nano-scale in functional layers up to systems level), including the development of accelerated testing procedures; - socio-economic and cross-cutting issues: public health, public acceptance, economics, market introduction; system studies on the benefits of FCH technologies to national and international energy supply. The training programme can build on the vast investments made by the participating universities in the past and facilitated by EPSRC, EU, industry and private funds. The laboratory infrastructure is up to date and fully enables the work of the student cohort. Industry funding is used to complement the EPSRC funding and add studentships on top of the envisaged 52 placements. The Centre will emphasise the importance of networking and exchange of information across the scientific and engineering field and thus interacts strongly with the EPSRC-SUPERGEN Hub in Fuel Cells and Hydrogen, thus integrating the other UK universities active in this research area, and also encourage exchanges with other European and international training initiatives. The modules will be accessible to professionals from the interacting industry in order to foster exchange of students with their peers in industry.

Over the last decades manufacturing in UK regions has been exposed to intense global competition, particularly as a consequence of trade liberalisation. At the same time, there is an increasing recognition that regions play a central role in national development, and there are mounting pressures on regions' ability to independently strategise and interconnect globally. These trends are particularly visible in the redistribution of power and funding from national to local government currently occurring through the so-called devolution deals, and through the emergence of Local Enterprise Partnerships that since 2010 have succeeded Regional Development Agencies. The renaissance of industry and manufacturing and the recognition that industry plays a central role in job creation, growth, and regions' economic recovery is also a priority in the policy agenda, with the 'Northern Powerhouse' strategy dominating the political lexicon, and setting the ambition to deliver business and enterprise growth with economic benefits for local communities. However, without adequate technology foresight and the identification of emergent technologies that may lead to innovations in practice, industrial manufacturing regions face the challenge of industrial stagnation and the threats of global outsourcing. Therefore, it is critical for regions to overcome the debilitating problem of poor innovation capabilities reinforced by the frequent overspecialisation of the knowledge infrastructure in these areas. It is also necessary to identify the technology enablers that may lead to opportunities for development and growth: the upgrading or revitalisation of businesses; the development of new business activities in areas related to the existing industries; or new industries based in new technologies. Focusing on Sheffield City Region as an internationally recognised manufacturing hub, and on the Advance Manufacturing and Materials sector, this project will generate new knowledge and procedural solutions to the extremely important issue relating to the enhancement of a region's ability to identify and exploit knowledge of technological innovations, in order to maximise competitiveness and sustainability. Working closely with firms, local enterprise partnerships, policy makers and innovation experts, the project focuses on the understanding and development of concrete regional practices and processes for identifying, transferring and integrating technological innovations. This set of practices and processes includes the identification of relevant emergent technologies, the production of visions concerning their applicability (e.g. ability to generate product, processes or business innovations), and the contextualisation and application of the knowledge produced (brokerage activities) to allow exploitation and use in practice by firms.

Doctoral Training Partnerships: a range of postgraduate training is funded by the Research Councils. For information on current funding routes, see the common terminology at https://www.ukri.org/apply-for-funding/how-we-fund-studentships/. Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.

Doctoral Training Partnerships: a range of postgraduate training is funded by the Research Councils. For information on current funding routes, see the common terminology at https://www.ukri.org/apply-for-funding/how-we-fund-studentships/. Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.

Bayesian inference is a process which allows us to extract information from data. The process uses prior knowledge articulated as statistical models for the data. We are focused on developing a transformational solution to Data Science problems that can be posed as such Bayesian inference tasks. An existing family of algorithms, called Markov chain Monte Carlo (MCMC) algorithms, offer a family of solutions that offer impressive accuracy but demand significant computational load. For a significant subset of the users of Data Science that we interact with, while the accuracy offered by MCMC is recognised as potentially transformational, the computational load is just too great for MCMC to be a practical alternative to existing approaches. These users include academics working in science (e.g., Physics, Chemistry, Biology and the social sciences) as well as government and industry (e.g., in the pharmaceutical, defence and manufacturing sectors). The problem is then how to make the accuracy offered by MCMC accessible at a fraction of the computational cost. The solution we propose is based on replacing MCMC with a more recently developed family of algorithms, Sequential Monte Carlo (SMC) samplers. While MCMC, at its heart, manipulates a single sampling process, SMC samplers are an inherently population-based algorithm that manipulates a population of samples. This makes SMC samplers well suited to the task of being implemented in a way that exploits parallel computational resources. It is therefore possible to use emerging hardware (e.g., Graphics Processor Units (GPUs), Field Programmable Gate Arrays (FPGAs) and Intel's Xeon Phis as well as High Performance Computing (HPC) clusters) to make SMC samplers run faster. Indeed, our recent work (which has had to remove some algorithmic bottlenecks before making the progress we have achieved) has shown that SMC samplers can offer accuracy similar to MCMC but with implementations that are better suited to such emerging hardware. The benefits of using an SMC sampler in place of MCMC go beyond those made possible by simply posing a (tough) parallel computing challenge. The parameters of an MCMC algorithm necessarily differ from those related to a SMC sampler. These differences offer opportunities for SMC samplers to be developed in directions that are not possible with MCMC. For example, SMC samplers, in contrast to MCMC algorithms, can be configured to exploit a memory of their historic behaviour and can be designed to smoothly transition between problems. It seems likely that by exploiting such opportunities, we will generate SMC samplers that can outperform MCMC even more than is possible by using parallelised implementations alone. Our interactions with users, our experience of parallelising SMC samplers and the preliminary results we have obtained when comparing SMC samplers and MCMC make us excited about the potential that SMC samplers offer as a "New Approach for Data Science". Our current work has only begun to explore the potential offered by SMC samplers. We perceive significant benefit could result from a larger programme of work that helps us understand the extent to which users will benefit from replacing MCMC with SMC samplers. We propose a programme of work that combines a focus on users' problems with a systematic investigation into the opportunities offered by SMC samplers. Our strategy for achieving impact comprises multiple tactics. Specifically, we will: use identified users to act as "evangelists" in each of their domains; work with our hardware-oriented partners to produce high-performance reference implementations; engage with the developer team for Stan (the most widely-used generic MCMC implementation); work with the Industrial Mathematics Knowledge Transfer Network and the Alan Turing Institute to engage with both users and other algorithmic developers.