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.

Our vision is to use continuous photochemistry and electrochemistry to transform how fine chemicals, agrochemicals and pharmaceuticals are manufactured in the UK. We aim to minimize the amount of chemicals, solvents and processing steps needed to construct complex molecules. We will achieve this by exploiting light and/or electricity to promote more specific chemical transformations and cleaner processes. By linking continuous photochemistry and electro-chemistry with thermal flow chemistry and environmentally acceptable solvents, we will create a toolkit with the power to transform all aspects of chemical synthesis from initial discovery through to chemical manufacturing of high-value molecules. The objective is to increase efficiency in terms of both atoms and energy, resulting in lower cost, low waste, low solvent footprints and shorter manufacturing routes. Historically photo- and electro-chemistry have been under-utilised in academia and industry because they are perceived to be complicated to use, difficult to scale up and engineer into viable processes despite their obvious environmental, energy and cost benefits. We will combine the strategies and the skills needed to overcome these barriers and will open up new areas of science, and deliver a step-change (i) providing routes to novel molecular architectures, hard to reach or even inaccessible by conventional methodologies, (ii) eliminating many toxic reagents by rendering them unnecessary, (iii) minimizing solvent usage, (iv) promoting new methodologies for synthetic route planning. Our proposal is supported by 21 industrial partners covering a broad range of sectors of the chemistry-using industries who are offering £1.23M in-kind support. Therefore, we will study a broad range of reactions to provide a clear understanding of the most effective areas for applying our techniques; we will evaluate strategies for altering the underlying photophysics and kinetics so as to accelerate the efficiency of promising reactions; we will transform our current designs of photochemical and electrochemical reactors, with a combination of engineering, modelling and new fabrication techniques to maximize their efficiency and to provide clear opportunities for scale-up; we will exploit on-line analytics to accelerate the optimisation of continuous photochemical and electrochemical reactions; we will design and build a new generation of reactors for new applications; we will identify the most effective strategies for linking our reactors into integrated multi-step continuous processes with minimized waste; we will demonstrate this integration on at least one synthesis of a representative pharmaceutical target molecule on a larger scale; we will apply a robust series of sustainability metrics to benchmark our approaches against current manufacturing.

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.

Wearable neurotechnology utilization is expected to increase dramatically in the coming years, with applications in enabling movement-independent control and communication, rehabilitation, treating disease and improving health, recreation and sport among others. There are multiple driving forces:- continued advances in underlying science and technology; increasing demand for solutions to repair the nervous system; increase in the ageing population worldwide producing a need for solutions to age-related, neurodegenerative disorders, and "assistive" brain-computer interface (BCI) technologies; and commercial demand for nonmedical BCIs. There is a significant opportunity for the UK to lead in the development of AI-enabled neurotechnology R&D. There are a number of key challenges to be addressed, mainly associated with the complexity of signals measured from the brain. AI has the potential to revolutionise the neurotechnology industry and neurotechnology presents an excellent challenge for AI. This fellowship will build on the award-winning AI and neurotechnology research of the fellow and offer real potential for impact through established clinical partnerships and in the neurotechnology industry. The objective of this project is to build on award-winning AI and neurotechnology R&D to address key shortcomings of neurotechnology that limit its widespread use and adoption using a range of key neural network technologies in a state-of-the-art framework for processing neural signals developed by the proposed fellow. The AI technologies developed for neurotechnology will be applied across sectors to demonstrate translational AI through engagement with at least 10 companies across at least 5 sectors during the fellowship, to demonstrate societal and economic benefit and interdisciplinary and translational AI skills development. The project has multiple industry, clinical and academic partners and is expected to produce world-leading AI technologies and propel the fellow to world-leading status in developing AI for neurotechnology which will impact widely. A major focus of the project is ensuring the expectations of the fellow role are met. This includes:- -Ensuring the processes and resources are in place to build a world-leading profile by the end of the fellowship; -Focusing on planning research of the team as new results emerge and hypothesis are tested, to refine and develop a high-quality programme of ambitious, novel and creative research, in AI-enabled Neurotechnology. Specific focus will be ensuring meticulous planning, execution and follow-up to produce world-leading results; -Continuing to perform my leadership role as director of the ISRC and leader of the data analytics theme, expanding the team and actively seek to develop into a position of higher leadership of the research agenda at Ulster, and in the national and international research community; -Focusing on strengthening relationships and collaborations with colleagues in industry and academia, and maximising the potential for flexible career paths for researchers within the team -Acting as an ambassador and advocate for AI, science and ED&I including by continuing to actively provide opinions and engaging with questions around AI and ethics, and responsible research and innovation (RRI). A focus will be embedding this throughout the activities of the fellowship but across the region and internationally; -Seeking to engage with and influence the strategic direction of the UK AI research and innovation landscape through engagement with their peers, policymakers, and other stakeholders including the public through. -Ensuring that the fundamental research is developed to have a high likelihood of impact on UK society/economy through trials across a range of patient groups to develop the evidence base and transfer of intellectual property to products, in particular through NeuroCONCISE Ltd, a main project partner.