
uFraction8 Limited
uFraction8 Limited
1 Projects, page 1 of 1
assignment_turned_in Project2019 - 2028Partners:Technical University Berlin, Intel UK, Moody's Analytics UK Ltd, Johnson Matthey, UP +98 partnersTechnical University Berlin,Intel UK,Moody's Analytics UK Ltd,Johnson Matthey,UP,National Physical Laboratory,WEST Beer,Oliver Wyman,Brainnwave Ltd,Procter & Gamble Limited (P&G UK),SNH,Office of Gas and Electricity Markets,Ocean Science Consulting,OpenGoSim,Berlin University of Technology,NM Group,HMG,WEST Beer,National Health Service Scotland,Intel UK,James Hutton Institute,Aberdeen Standard Investments,NERC British Geological Survey,Brown University,Berlin University of Technology,National Wildlife Research Institute,Norwegian University of Science and Technology Science and Technology,British Geological Survey,IBM Research,Royal Bank of Scotland Plc,THE JAMES HUTTON INSTITUTE,Cresset (United Kingdom),CRESSET BIOMOLECULAR DISCOVERY LIMITED,Ofgem,National School of Bridges ParisTech,Johnson Matthey (United Kingdom),BioSS (Biomaths and Stats Scotland),AkzoNobel UK,Dassauly Systemes BIOVIA,Leonardo MW Ltd,Royal Bank of Scotland Plc,Leonardo MW Ltd,Brainnwave Ltd,Intel Corporation (UK) Ltd,nVIDIA,Aberdeen Standard Investments,NPL,NPL,BioSS (Biomaths and Stats Scotland),Technical University of Denmark,University of Turin,Brown University,uFraction8 Limited,Ocean Science Consulting,Infineum UK,Oliver Wyman,National School of Bridges ParisTech,uFraction8 Limited,Nvidia (United States),James Hutton Institute,Utrecht University,The Data Lab,University of Edinburgh,PROCTER & GAMBLE TECHNICAL CENTRES LIMITED,AkzoNobel UK,Moody's Analytics UK Ltd,NM Group,IBM Research,Utrecht University,Dassault Systemes Biovia Ltd,Forestry Commission England,PROCTER & GAMBLE TECHNICAL CENTRES LIMITED,University of Turin,Forestry Commission England,Infineum UK,TU Wien,OpenGoSim,Technical University of Denmark,National Wildlife Research Institute,NERC British Geological Survey,Infineum (United Kingdom),Duke University,SNH,NHS NATIONAL SERVICES SCOTLAND,NTNU (Norwegian Uni of Sci & Technology),DTU,Duke University,AkzoNobel (United Kingdom),DEFRA,Norwegian University of Science and Technology,The Data Lab,McLaren Applied Technologies,Johnson Matthey Plc,NatureScot (Scottish Natural Heritage),James Hutton Institute,AkzoNobel UK,NHS National Services Scotland,TUW,University of Turin,Royal Bank of Scotland (United Kingdom),McLaren Honda (United Kingdom),CRESSET BIOMOLECULAR DISCOVERY LIMITED,Vienna University of TechnologyFunder: UK Research and Innovation Project Code: EP/S023291/1Funder Contribution: 6,112,270 GBPThe Centre for Doctoral Training MAC-MIGS will provide advanced training in the formulation, analysis, and implementation of state-of-the-art mathematical and computational models. The vision for the training offered is that effective modern modelling must integrate data with laws framed in explicit, rigorous mathematical terms. The CDT will offer 76 PhD students an intensive 4-year training and research programme that equips them with the skills needed to tackle the challenges of data-intensive modelling. The new generation of successful modelling experts will be able to develop and analyse mathematical models, translate them into efficient computer codes that make best use of available data, interpret the results, and communicate throughout the process with users in industry, commerce and government. Mathematical and computational models are at the heart of 21st-century technology: they underpin science, medicine and, increasingly, social sciences, and impact many sectors of the economy including high-value manufacturing, healthcare, energy, physical infrastructure and national planning. When combined with the enormous computing power and volume of data now available, these models provide unmatched predictive tools which capture systematically the experimental and observational evidence available. Because they are based on sound deductive principles, they are also the only effective tool in many problems where data is either sparse or, as is often the case, acquired in conditions that differ from the relevant real-world scenarios. Developing and exploiting these models requires a broad range of skills - from abstract mathematics to computing and data science - combined with expertise in application areas. MAC-MIGS will equip its students with these skills through a broad programme that cuts across disciplinary boundaries to include mathematical analysis - pure, applied, numerical and stochastic - data-science and statistics techniques and the domain-specific advanced knowledge necessary for cutting-edge applications. MAC-MIGS students will join the broader Maxwell Institute Graduate School in its brand-new base located in central Edinburgh. They will benefit from (i) dedicated academic training in subjects that include mathematical analysis, computational mathematics, multi-scale modelling, model reduction, Bayesian inference, uncertainty quantification, inverse problems and data assimilation, and machine learning; (ii) extensive experience of collaborative and interdisciplinary work through projects, modelling camps, industrial sandpits and internships; (iii) outstanding early-career training, with a strong focus on entrepreneurship; and (iv) a dynamic and forward-looking community of mathematicians and scientists, sharing strong values of collaboration, respect, and social and scientific responsibility. The students will integrate a vibrant research environment, closely interacting with some 80 MAC-MIGS academics comprised of mathematicians from the universities of Edinburgh and Heriot-Watt as well as computer scientists, engineers, physicists and chemists providing their own disciplinary expertise. Students will benefit from MAC-MIGS's diverse network of more than 30 industrial and agency partners spanning a broad spectrum of application areas: energy, engineering design, finance, computer technology, healthcare and the environment. These partners will provide internships, development programmes and research projects, and help maximise the impact of our students' work. Our network of academic partners representing ten leading institutions in the US and Europe, will further provide opportunities for collaborations and research visits.
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