Statistics, inverse problems, uncertainty quantification and data science
Our researchers combine statistical and mathematical models, data science and computational algorithms to solve realworld problems.
PhD projects
We welcome applications for PhD study in all areas of statistics, inverse problems, uncertainty quantification and mathematical aspects of data science.
Before applying, visit the 'areas of expertise' pages listed below to find out more about potential PhD supervisors.
PhD enquiries related to this theme can be directed to Dr Stefan Güttel (applied and numerical analysis projects) and Professor Korbinian Strimmer (statistics projects).
To tackle the complex challenges of modern society, and due to increases in available computing power and data, traditional disciplines in statistics and applied mathematics are merging into exciting new interdisciplinary fields.
Building on a strong tradition of statistics and numerical analysis in Manchester, our researchers work in a range of interconnected areas, combining statistics, mathematical models, data science and computational algorithms to solve problems arising in engineering, life sciences and public health.
From methodological research to data analysis and development of software, we have expertise in the following core areas:
Areas of expertise

Statistics
Statistics is concerned with the analysis and interpretation of data, the design of experiments and decisionmaking under uncertainty.
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Inverse problems
In inverse problems researchers look inside solid objects or deduce complex models from data using mathematics.
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Uncertainty quantification
Uncertainty quantification is a modern interdisciplinary science that cuts across traditional research groups and combines statistics, numerical analysis and computational applied mathematics.
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Research outputs
Find the Department's recent publications in the University's database.
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Postgraduate research
Discover the PhD opportunities available in the Department of Mathematics.
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Research seminars
Research seminars on topics associated with statistics, inverse problems, uncertainty quantification and data science take place regularly in the following series: