This PhD project at the University of Edinburgh, co-supervised by Dr M Nicholson and Prof MT Taylor, offers an exciting opportunity to apply stochastic modelling to the analysis of cancer mutation data. The DNA in human cells accumulates mutations throughout life, and understanding the causes of these mutations is crucial for both fundamental biology and clinical decision making. With the advent of large-scale mutation datasets, researchers are now able to tackle these questions with unprecedented depth and rigor. The project will focus on developing new mathematical models to study how DNA repair processes influence the patterns of mutations observed in cancer. Students will employ analytic techniques and stochastic simulation to explore these models, and will use advanced statistical inference methods—including likelihood and Bayesian approaches—to analyze mutation datasets from both human cancers and experimental systems. This multidisciplinary research will be conducted in collaboration between the School of Mathematics and the MRC Human Genetics Unit, ensuring that the mathematical work remains highly relevant to biological questions. Ideal candidates will be enthusiastic about probability theory, stochastic modelling (such as Markov processes), and statistical inference with large datasets. The project is designed to help students develop their computational skills, and no prior biological knowledge is required—just a willingness to learn and engage with biologically meaningful research questions. The position provides a unique opportunity for training at the intersection of mathematics, statistics, and cancer biology, preparing graduates for careers in academic research, data science, or biomedical analysis. While funding details are not specified in the announcement, applicants are encouraged to contact Dr M Nicholson ([email protected]) for further information about the project and application process. The application deadline is January 19, 2026, and interested candidates should review the project details and ensure their background aligns with the requirements before applying. For more information and to begin your application, visit the official FindAPhD project page.