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.

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.

The Transforming the Foundation Industries Challenge has set out the background of the six foundation industries; cement, ceramics, chemicals, glass, metals and paper, which produce 28 Mt pa (75% of all materials in our economy) with a value of £52Bn but also create 10% of UK CO2 emissions. These materials industries are the root of all supply chains providing fundamental products into the industrial sector, often in vertically-integrated fashion. They have a number of common factors: they are water, resource and energy-intensive, often needing high temperature processing; they share processes such as grinding, heating and cooling; they produce high-volume, often pernicious waste streams, including heat; and they have low profit margins, making them vulnerable to energy cost changes and to foreign competition. Our Vision is to build a proactive, multidisciplinary research and practice driven Research and Innovation Hub that optimises the flows of all resources within and between the FIs. The Hub will work with communities where the industries are located to assist the UK in achieving its Net Zero 2050 targets, and transform these industries into modern manufactories which are non-polluting, resource efficient and attractive places to be employed. TransFIRe is a consortium of 20 investigators from 12 institutions, 49 companies and 14 NGO and government organisations related to the sectors, with expertise across the FIs as well as energy mapping, life cycle and sustainability, industrial symbiosis, computer science, AI and digital manufacturing, management, social science and technology transfer. TransFIRe will initially focus on three major challenges: 1 Transferring best practice - applying "Gentani": Across the FIs there are many processes that are similar, e.g. comminution, granulation, drying, cooling, heat exchange, materials transportation and handling. Using the philosophy Gentani (minimum resource needed to carry out a process) this research would benchmark and identify best practices considering resource efficiencies (energy, water etc.) and environmental impacts (dust, emissions etc.) across sectors and share information horizontally. 2 Where there's muck there's brass - creating new materials and process opportunities. Key to the transformation of our Foundation Industries will be development of smart, new materials and processes that enable cheaper, lower-energy and lower-carbon products. Through supporting a combination of fundamental research and focused technology development, the Hub will directly address these needs. For example, all sectors have material waste streams that could be used as raw materials for other sectors in the industrial landscape with little or no further processing. There is great potential to add more value by "upcycling" waste by further processes to develop new materials and alternative by-products from innovative processing technologies with less environmental impact. This requires novel industrial symbioses and relationships, sustainable and circular business models and governance arrangements. 3 Working with communities - co-development of new business and social enterprises. Large volumes of warm air and water are produced across the sectors, providing opportunities for low grade energy capture. Collaboratively with communities around FIs, we will identify the potential for co-located initiatives (district heating, market gardening etc.). This research will highlight issues of equality, diversity and inclusiveness, investigating the potential from societal, environmental, technical, business and governance perspectives. Added value to the project comes from the £3.5 M in-kind support of materials and equipment and use of manufacturing sites for real-life testing as well as a number of linked and aligned PhDs/EngDs from HEIs and partners This in-kind support will offer even greater return on investment and strongly embed the findings and operationalise them within the sector.