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| Numerical Methods for Partial and Stochastic Differential Equations | ||||||||||||
This PhD project involves the development and analysis of approximate methods for partial and stochastic differential equations, along with the linear algebra needed to make them work efficiently. The goal is to develop highly efficient numerical algorithms suitable for modern high performance computers, from the now commonplace desktop dual and quad-core computers, through the upcoming multi-core desktop machines and on to high-end national research laboratory scale computers. The equations arise in the modelling of real-world problems (e.g. in the energy sector, medicine and biology) and the project will involve interactions with the end-users. The studentship is provided by Heriot-Watt University in support of the ground-breaking Numerical Algorithms and Intelligent Software (NAIS) project, which is a collaboration of Heriot-Watt, Edinburgh and Strathclyde Universities involving many PhD students, postdocs and academic staff spread across the three Universities. The subject areas covered by NAIS are numerical analysis, computer science and high performance computing and we also work with partners from other disciplines. The work for this PhD project is mathematical. We offer a lively, supportive and highly research-active environment for graduate students in Mathematics at Heriot-Watt, embedded in the Maxwell Institute for Mathematical Sciences partnership with the University of Edinburgh as well as the NAIS project. Students with a good first degree or MSc in mathematics with knowledge of numerical analysis, differential equations and some analysis would be ideal. Training is available in numerical analysis and in high performance computing. Funding Notes The stipend will be at standard EPSRC rates for eligible students (i.e. UK and EU), currently £13,290 per year. The Numerical Algorithms and Intelligent Software homepage is www.nais.org.uk The Heriot-Watt University Mathematics Department homepage is www.ma.hw.ac.uk/maths.html |
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