Are you interested in developing algorithms and scalable statistical methods for molecular data analysis, including phylogenomic methods, fitness landscape inference and analysis, genetic interactions, and other computationally demanding areas of genomics and related fields? Or working on mathematical foundations of these methods? Want to live in New Zealand’s most spectacular region of Canterbury? Get in touch with us to express your interest in joining the lab.

We are currently accepting applications for MSc, PhD, and postdoc positions and scholarships. The following projects are on offer:

  • Phylogenomic methods and tools for within-patient tumour evolution analysis. This projects includes the development and applications of computational phylogenomic inference methods from cancer data. We will combine and adapt computational approaches in phylogenomics and population dynamics to create robust methods and efficient tools for evolutionary analysis of heterogeneous clinical data, including various types of molecular sequencing data, images, and clinical history.

  • Evolutionary landscapes of microbial resistance. This project is aimed at developing mathematical and computational methods to analyse mutational pathways in bacteria responsible for the development of antibiotic and other types of resistance. We will specifically focus on studying the combinatorially complicated structure of interactions within and between mutational pathways/ genes. The ultimate goal will be to computationally predict evolutionary trajectories of certain strains of bacteria under antibiotic pressure, and suggest possible strategies to prevent the development of resistance.

  • Online algorithms in computational biology. This project will focus on certain classes of computational inference methods, typically within the domains of phylogenomics and fitness landscapes, with the goal to design their scalable online versions. We will develop mathematical approaches, algorithms, and data structures for popular bioinformatic inference methods to make them robust to changes in data through time.

Please feel free to drop Alex a line if you want to learn more about these and/ or what to join the lab with your own project.