Discrete random models in evolutionary biology

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'.
Supervisors
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
programming
some background in modern molecular evolutionary biology
Does the project come with funding
No
Final date for receiving applications
Ongoing
Keywords
Mathematical Biology; Biology