EPSRC Centre for Doctoral Training in Mathematical Modelling, Analysis and Computation (MAC-MIGS)
Lead Research Organisation:
University of Edinburgh
Department Name: Sch of Mathematics
Abstract
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.
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.
Planned Impact
MAC-MIGS develops computational modelling and its application to a range of economic sectors, including high-value manufacturing, energy, finance and healthcare. These fields contribute over £500 billion to the UK economy. The CDT involves collaborations with more than a dozen companies and organisations, including large corporations (AkzoNobel, IBM, Dassault, P&G, Aberdeen Standard Investments, Intel), mid-size firms, particularly in the engineering and power sectors (NM Group, which provides monitoring services to power grid operators in 30 countries, Artemis Intelligent Power, the world leader in digital displacement hydraulics, Leonardo, a provider of defense, security and aerospace services, and Oliver Wymans, a management consultancy firm) and startups such as Brainnwave, which develops data-modelling solutions, and Opengosim which designs state-of-the-art and massively parallel software for subsurface reservoir simulation. Government and other agencies involved will include the British Geological Survey, Forestry Commission, James Hutton Institute, and Scottish National Heritage. Engagement will be via internships, short projects and PhD projects. BIS has stated that "Organisations using computer generated modelling and simulations and Big Data analytics create better products, get greater insights, and gain competitive advantage over traditional development processes". Our partners share this vision and are keen to develop deeper collaborations with us over the duration of the CDT.
Our CDT will achieve the following:
- Produce 76 highly skilled mathematical scientists and professionals, ready to take up positions in academia or in companies such as our partners. The students will have exposure to projects, modelling camps and high-level international collaborations.
- Deliver economic and societal benefits through student research projects developed in close collaboration with our partners in industry, business and government and other agencies.
- Create pathways for impact on computer science, chemistry, physics and engineering by involving interdisciplinary partners from Heriot-Watt and Edinburgh Universities in the supervision and training of our students.
- Organise a large number of lectures and seminars which will be open to staff and students of the two universities. Such lectures will inform the wide university communities about the state-of-the-art in computational and mathematical modelling.
- Work with other CDTs both in Edinburgh and beyond to organise a series of workshops for undergraduates, intended to foster an increased uptake of PhD studentship places in technical areas by female students and those from ethnic minorities, with potential impact on the broader UK CDT landscape.
- Organise industrial sandpits and modelling camps which offer the possibility for our partners to present a challenge arising in their work, and to explore innovative ways to tackle that challenge, fully involving the CDT students. This will kick-start a change in the corporate mindset by exposing the relevant staff to new approaches.
- Develop a new course, "Entrepreneurship for Doctoral Students in the Mathematical Sciences" in conjunction with Converge Challenge (Scotland's largest entrepreneurial training programme) and UoE's School of Business. This and other support measures will develop an innovation culture and facilitate the translation of our students' ideas into commercial activities.
Our CDT will achieve the following:
- Produce 76 highly skilled mathematical scientists and professionals, ready to take up positions in academia or in companies such as our partners. The students will have exposure to projects, modelling camps and high-level international collaborations.
- Deliver economic and societal benefits through student research projects developed in close collaboration with our partners in industry, business and government and other agencies.
- Create pathways for impact on computer science, chemistry, physics and engineering by involving interdisciplinary partners from Heriot-Watt and Edinburgh Universities in the supervision and training of our students.
- Organise a large number of lectures and seminars which will be open to staff and students of the two universities. Such lectures will inform the wide university communities about the state-of-the-art in computational and mathematical modelling.
- Work with other CDTs both in Edinburgh and beyond to organise a series of workshops for undergraduates, intended to foster an increased uptake of PhD studentship places in technical areas by female students and those from ethnic minorities, with potential impact on the broader UK CDT landscape.
- Organise industrial sandpits and modelling camps which offer the possibility for our partners to present a challenge arising in their work, and to explore innovative ways to tackle that challenge, fully involving the CDT students. This will kick-start a change in the corporate mindset by exposing the relevant staff to new approaches.
- Develop a new course, "Entrepreneurship for Doctoral Students in the Mathematical Sciences" in conjunction with Converge Challenge (Scotland's largest entrepreneurial training programme) and UoE's School of Business. This and other support measures will develop an innovation culture and facilitate the translation of our students' ideas into commercial activities.
Organisations
- University of Edinburgh (Lead Research Organisation)
- IBM Research (Project Partner)
- Intel Corporation (UK) Ltd (Project Partner)
- Moody's Analytics UK Ltd (Project Partner)
- Utrecht University (Project Partner)
- McLaren Applied Technologies (Project Partner)
- Aberdeen Standard Investments (Project Partner)
- Technical University of Denmark (Project Partner)
- Infineum UK Ltd (Project Partner)
- University of Turin (Project Partner)
- The Data Lab (Project Partner)
- Dassault Systemes Biovia Ltd (Project Partner)
- British Geological Survey (Project Partner)
- Royal Bank of Scotland Plc (Project Partner)
- Procter & Gamble Limited (P&G UK) (Project Partner)
- Leonardo MW Ltd (Project Partner)
- NHS NATIONAL SERVICES SCOTLAND (Project Partner)
- NatureScot (Project Partner)
- Ocean Science Consulting (Project Partner)
- Brainnwave Ltd (Project Partner)
- Vienna University of Technology (Project Partner)
- National School of Bridges ParisTech (Project Partner)
- NTNU (Norwegian Uni of Sci & Technology) (Project Partner)
- National Physical Laboratory NPL (Project Partner)
- University of Potsdam (Project Partner)
- Oliver Wyman (Project Partner)
- Forestry Commission UK (Project Partner)
- Brown University (Project Partner)
- BioSS (Biomaths and Stats Scotland) (Project Partner)
- Duke University (Project Partner)
- OpenGoSim (Project Partner)
- Cresset BioMolecular Discovery Ltd (Project Partner)
- Technical University Berlin (Project Partner)
- AkzoNobel UK (Project Partner)
- National Wildlife Research Institute (Project Partner)
- Johnson Matthey (Project Partner)
- WEST Beer (Project Partner)
- Ofgem (Project Partner)
- NM Group (Project Partner)
- uFraction8 Limited (Project Partner)
- THE JAMES HUTTON INSTITUTE (Project Partner)
- nVIDIA (Project Partner)
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/S023291/1 | 30/09/2019 | 30/03/2028 | |||
| 2284952 | Studentship | EP/S023291/1 | 31/08/2019 | 30/11/2023 | Martin Brolly |
| 2277589 | Studentship | EP/S023291/1 | 31/08/2019 | 30/08/2024 | Samuel Bonsor |
| 2278918 | Studentship | EP/S023291/1 | 31/08/2019 | 30/05/2024 | Mason Pearce |
| 2278925 | Studentship | EP/S023291/1 | 31/08/2019 | 30/03/2023 | Michael Redenti |
| 2279089 | Studentship | EP/S023291/1 | 31/08/2019 | 29/06/2024 | Toyo Vignal |
| 2278936 | Studentship | EP/S023291/1 | 31/08/2019 | 30/11/2023 | Jonathan Spence |
| 2278010 | Studentship | EP/S023291/1 | 31/08/2019 | 30/05/2024 | Niamh Graham |
| 2278824 | Studentship | EP/S023291/1 | 31/08/2019 | 29/02/2024 | Rene Lohmann |
| 2277653 | Studentship | EP/S023291/1 | 31/08/2019 | 30/03/2022 | Joseph Colvin |
| 2277939 | Studentship | EP/S023291/1 | 31/08/2019 | 30/11/2023 | Xue Gong |
| 2278947 | Studentship | EP/S023291/1 | 31/08/2019 | 31/12/2023 | Iain Souttar |
| 2284962 | Studentship | EP/S023291/1 | 31/08/2019 | 31/12/2023 | Viktoria Freingruber |
| 2277802 | Studentship | EP/S023291/1 | 31/08/2019 | 30/08/2023 | Aigerim Davletzhanova |
| 2436417 | Studentship | EP/S023291/1 | 31/08/2020 | 30/08/2024 | Andres Miniguano Trujillo |
| 2436448 | Studentship | EP/S023291/1 | 31/08/2020 | 30/11/2024 | Conor Osborne |
| 2436120 | Studentship | EP/S023291/1 | 31/08/2020 | 30/08/2024 | Donald Hobson |
| 2436378 | Studentship | EP/S023291/1 | 31/08/2020 | 30/05/2022 | Ben MacVicar |
| 2435641 | Studentship | EP/S023291/1 | 31/08/2020 | 28/02/2025 | Andrew Cleary |
| 2436431 | Studentship | EP/S023291/1 | 31/08/2020 | 30/08/2024 | Fraser O'Brien |
| 2436511 | Studentship | EP/S023291/1 | 31/08/2020 | 30/08/2024 | Peter Whalley |
| 2435550 | Studentship | EP/S023291/1 | 31/08/2020 | 31/12/2024 | Rebecca Akeresola |
| 2436507 | Studentship | EP/S023291/1 | 31/08/2020 | 31/12/2024 | Maia Trower |
| 2436169 | Studentship | EP/S023291/1 | 31/08/2020 | 31/10/2023 | Johanna Jarvsoo |
| 2435649 | Studentship | EP/S023291/1 | 31/08/2020 | 30/08/2024 | Michael Cox |
| 2927457 | Studentship | EP/S023291/1 | 31/08/2020 | 30/08/2024 | Karolina Benkova |
| 2436161 | Studentship | EP/S023291/1 | 31/08/2020 | 30/08/2024 | Elizabeth Howell |
| 2436342 | Studentship | EP/S023291/1 | 31/08/2020 | 30/08/2024 | Aikaterini Karoni |
| 2568884 | Studentship | EP/S023291/1 | 31/08/2021 | 30/08/2025 | Mary Eby |
| 2593526 | Studentship | EP/S023291/1 | 31/08/2021 | 30/05/2026 | Christopher Oldnall |
| 2568886 | Studentship | EP/S023291/1 | 31/08/2021 | 30/08/2025 | Lucas Beerens |
| 2590986 | Studentship | EP/S023291/1 | 31/08/2021 | 30/08/2025 | Rasheed Ibraheem |
| 2593987 | Studentship | EP/S023291/1 | 31/08/2021 | 30/08/2025 | Lukasz Sliwinski |
| 2593534 | Studentship | EP/S023291/1 | 31/08/2021 | 30/08/2025 | Alexander Richardson |
| 2565944 | Studentship | EP/S023291/1 | 31/08/2021 | 29/09/2025 | Elliot Addy |
| 2593952 | Studentship | EP/S023291/1 | 31/08/2021 | 30/08/2025 | William Sumners |
| 2566035 | Studentship | EP/S023291/1 | 31/08/2021 | 30/08/2025 | Meritxell Brunet Guasch |
| 2593961 | Studentship | EP/S023291/1 | 31/08/2021 | 30/08/2025 | Sofie Verhees |
| 2593991 | Studentship | EP/S023291/1 | 31/08/2021 | 30/08/2025 | Jiaao Wang |
| 2591000 | Studentship | EP/S023291/1 | 31/08/2021 | 30/08/2025 | Alix Leroy |
| 2590949 | Studentship | EP/S023291/1 | 31/08/2021 | 30/08/2025 | Theo Lavier |
| 2568885 | Studentship | EP/S023291/1 | 31/08/2021 | 30/08/2025 | Bernhard Heinzelreiter |
| 2913974 | Studentship | EP/S023291/1 | 31/08/2021 | 30/08/2025 | Razvan Lascu |
| 2590989 | Studentship | EP/S023291/1 | 31/08/2021 | 30/08/2025 | Jacob Armstrong-Goodall |
| 2590990 | Studentship | EP/S023291/1 | 31/08/2021 | 30/08/2025 | Liam Llamazares Elias |
| 2568842 | Studentship | EP/S023291/1 | 31/08/2021 | 30/08/2025 | Anastasia Istratuca |
| 2737525 | Studentship | EP/S023291/1 | 31/03/2022 | 30/08/2025 | Mahya Meyari |
| 2784893 | Studentship | EP/S023291/1 | 31/08/2022 | 30/08/2026 | ines Demano |
| 2784978 | Studentship | EP/S023291/1 | 31/08/2022 | 30/08/2026 | Yiming Xi |
| 2784887 | Studentship | EP/S023291/1 | 31/08/2022 | 30/08/2026 | Nicolas Cassia Terrazo |
| 2784892 | Studentship | EP/S023291/1 | 31/08/2022 | 30/08/2026 | Chung Chu |
| 2784882 | Studentship | EP/S023291/1 | 31/08/2022 | 30/08/2026 | Jialun Cao |
| 2784904 | Studentship | EP/S023291/1 | 31/08/2022 | 14/03/2024 | Ria Dunn |
| 2784954 | Studentship | EP/S023291/1 | 31/08/2022 | 30/08/2026 | Ian Powell |
| 2784913 | Studentship | EP/S023291/1 | 31/08/2022 | 30/08/2026 | Brian Hennessy |
| 2784957 | Studentship | EP/S023291/1 | 31/08/2022 | 30/08/2026 | Zakee Sattar |
| 2784967 | Studentship | EP/S023291/1 | 31/08/2022 | 30/08/2026 | Mohammad Tabish |
| 2784962 | Studentship | EP/S023291/1 | 31/08/2022 | 30/08/2026 | Ognyan Simeonov |
| 2784974 | Studentship | EP/S023291/1 | 31/08/2022 | 30/08/2026 | Rowan Turner |
| 2784914 | Studentship | EP/S023291/1 | 31/08/2022 | 30/08/2026 | Kaitlyn Louth |
| 2784922 | Studentship | EP/S023291/1 | 31/08/2022 | 30/08/2026 | Joel-Pascal N'konzi |
| 2784920 | Studentship | EP/S023291/1 | 31/08/2022 | 30/08/2026 | Samuel Naylor |
| 2784915 | Studentship | EP/S023291/1 | 31/08/2022 | 30/08/2026 | Abhijeet Minz |
| 2784905 | Studentship | EP/S023291/1 | 30/09/2022 | 29/09/2026 | Sara Helal |
| 2884145 | Studentship | EP/S023291/1 | 31/08/2023 | 30/08/2027 | Andrea Meda |
| 2884296 | Studentship | EP/S023291/1 | 31/08/2023 | 30/08/2027 | Motahare Torki |
| 2884093 | Studentship | EP/S023291/1 | 31/08/2023 | 30/08/2027 | Jessica Codling |
| 2884397 | Studentship | EP/S023291/1 | 31/08/2023 | 30/08/2027 | Eylul Zorba |
| 2884144 | Studentship | EP/S023291/1 | 31/08/2023 | 30/08/2027 | Eirini Ioannou |
| 2884340 | Studentship | EP/S023291/1 | 31/08/2023 | 30/08/2027 | Kaicheng Zhang |
| 2884248 | Studentship | EP/S023291/1 | 31/08/2023 | 30/08/2027 | Jake Skelton |
| 2884252 | Studentship | EP/S023291/1 | 31/08/2023 | 30/08/2027 | Keren Tapper |
| 2884165 | Studentship | EP/S023291/1 | 31/08/2023 | 30/08/2027 | Eleni Michaelidou |
| 2884166 | Studentship | EP/S023291/1 | 31/08/2023 | 30/08/2027 | Oluwatoyosi Sadare |
| 2884320 | Studentship | EP/S023291/1 | 31/08/2023 | 30/08/2027 | Ross Walker |
| 2924217 | Studentship | EP/S023291/1 | 30/09/2023 | 29/09/2027 | Maame Ama Bainson |