Numerical analysis and scientific computing
Our researchers develop and analyse algorithms that compute numerical approximations and apply them to real-world problems.
We welcome applications for PhD study in all areas of numerical analysis and scientific computing.
PhD enquiries related to this theme can be directed to Dr Stefan Güttel.
Numerical analysis is the branch of rigorous mathematics that concerns the development and analysis of methods to compute numerical approximations to the solutions of mathematical problems. It is a broadly based discipline that sits at the interface between mathematical analysis and scientific computing.
Scientific computing describes the use of numerical simulation to study natural phenomena, complementing the more traditional experimental and theoretical approaches. Another broad discipline, it spans all the sciences with strong links to numerical analysis, computer science and software engineering.
Our work covers the breadth of these disciplines from fundamental theory and algorithm development through to implementation in open source software. Our researchers have expertise in the following areas.
Areas of expertise
Approximation theory is a key component of contemporary algorithms used in computational science and engineering.
Numerical linear algebra
Numerical linear algebra is at the heart of computational algorithms used in science and engineering, and in industry.
Scientific computing is the study of the techniques that underpin discipline-specific fields of computational science.
Uncertainty quantification is a modern inter-disciplinary science that cuts across traditional research groups and combines statistics, numerical analysis and computational applied mathematics.
Our staff, students and postgraduate researchers have access to a fantastic range of facilities across the University.
Find the Department's recent publications in the University's database.
Discover the PhD opportunities available in the Department of Mathematics.