Discrete random models in evolutionary biology

Host Faculty: Engineering
General Subject Area: Statistics and Data Science
Project Level: PhD
View the Research Website
FIND A PROJECT - Engineering

The last 3 decades have seen spectacular advances in our understanding of evolutionary biology, due largely to the wealth of molecular data (genes and genomes) being generated. Stochastic models are a fundamental tool to analyse this data, and the development of better models, and better methods of analysis requires a careful interplay of mathematics, algorithm development, statistics, and communication with biologists.

This project will aim to develop models and methods required to analyse new types of genomic data that are becoming available, and to explore approaches that build a 'network of life' rather than 'tree of life'.


Supervisor: Mike Steel

Key qualifications and skills

The precise project will depend on the skills and interests of the student, but any of the following background would be useful:

probability theory and statistics
discrete mathematics
algorithms and computer science
some background in modern molecular evolutionary biology

Does the project come with funding


Final date for receiving applications



Mathematical Biology; Biology