Mathematics for Real-World Systems CDT phase II funded by the EPSRC
We are delighted to announce a new EPSRC-funded phase of the Mathematics for Real-World Systems Centre for Doctoral Training. Over the next decade we will train fifty PhD students in the advanced quantitative skills and applied mathematical modelling critical to address the contemporary challenges arising from biomedicine and health sectors, modern industry and the digital economy.
MathSys II builds on the highly successful and inter-disciplinary MathSys CDT that was funded by both the EPSRC and MRC. Our second phase will focus on two cross-cutting methodological themes that are central to complex multi-scale systems prediction: modelling across spatial and temporal scales; and hybrid modelling integrating complex data and mechanistic models. These themes pervade many areas of active research and will shape mathematical and computational modelling for the coming decades. The CDT will address four application areas
(1) Quantitative biomedical research
(2) Mathematical epidemiology
(3) Socio-technical systems
(4) Advanced modelling and optimisation of industrial processes
The aim of the CDT is productive and impactful research engagement with our end-users. This has already been a distinguishing feature of the first phase of the MathSys CDT and has led to a very strong endorsement with over 25 external partners committing £1.5M in direct and in-kind support to MathSys II.
Though based in the Warwick Mathematics Institute, MathSys II is highly cross-disciplinary drawing core staff from Computer Science, Statistics and Physics, with 40+ supervisors from over 11 departments.
This is an ambitious cohort-based training programme that will equip the next generation of researchers with cutting-edge methodological toolkits and practical experience of external end-user engagement to address a broad variety of pressing real-world problems.
Our research degrees will equip you with:
- contemporary and highly sought mathematical skills
- the ability to understand and model real-world systems
- broad ways of analysing complex data sets
- multi-disciplinary experience and the option of specialising in key areas of research interest
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