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Details of Grant 

EPSRC Reference: EP/S023291/1
Title: EPSRC Centre for Doctoral Training in Mathematical Modelling, Analysis and Computation (MAC-MIGS)
Principal Investigator: Leimkuhler, Professor B
Other Investigators:
Duncan, Professor D Lord, Professor G Pelloni, Professor B
Teckentrup, Dr A Zygalakis, Dr KK Goddard, Dr B
Vanneste, Professor J Ottobre, Dr M Ptashnyk, Dr M
Researcher Co-Investigators:
Project Partners:
Aberdeen Standard Investments AKZO Nobel BioSS (Biomaths and Stats Scotland)
Brainnwave Ltd British Geological Survey Brown University
Cresset BioMolecular Discovery Ltd Dassault Systemes Duke University
Forestry Commission UK IBM UK Ltd Infineum UK Ltd
Intel Corporation Ltd Johnson Matthey Leonardo UK ltd
McLaren Applied Technologies Moody's Analytics UK Ltd National Physical Laboratory NPL
National School of Bridges ParisTech National Wildlife Research Institute NatureScot
NHS National Services Scotland NM Group NTNU (Norwegian Uni of Sci & Technology)
nVIDIA Ocean Science Consulting Ofgem
Oliver Wyman OpenGoSim Procter & Gamble
Royal Bank of Scotland Technical University Berlin Technical University of Denmark
The Data Lab The James Hutton Institute uFraction8 Limited
University of Potsdam University of Turin Utrecht University
Vienna University of Technology WEST Beer
Department: Sch of Mathematics
Organisation: University of Edinburgh
Scheme: Centre for Doctoral Training
Starts: 01 October 2019 Ends: 31 March 2028 Value (£): 6,384,736
EPSRC Research Topic Classifications:
Mathematical Analysis Numerical Analysis
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
07 Nov 2018 EPSRC Centres for Doctoral Training Interview Panel F – November 2018 Announced
Summary on Grant Application Form
The 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.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Organisation Website: http://www.ed.ac.uk