Research opportunities

The Centre for Computational Evolution is looking for students who are keen to develop expertise in using phylogenetics software to solve real world issues. 

Scholarships are regularly available as part of funded projects, please contact us for more details. 

Two new PhD positions

Bayesian integrative models of evolution

Two new PhD positions in computational evolution are available in the Centre for Computational Evolution at the University of Auckland to work on developing Bayesian integrative models of evolution that use data from genomic sequences, phenotypic data and the fossil record. The research will include the design and development of new mathematical and computational models for Bayesian phylogenetic inference. The successful candidates will work with an international team of computational biologists, evolutionary biologists and palaeontologists to both develop new methods, and test them on a number of exciting data sets.

How and when species came to be is the fundamental question in macroevolution. Attempts to answer it use a variety of data sources including genome sequences, morphology and fossil discoveries. Yet current methods are unable to exploit all this data, with different data sources often producing conflicting results. This project aims to create a unifying probabilistic framework that combines genomic, fossil and phenotypic data to give us the best possible understanding of evolutionary history. The PhD research will involve creating open-source software tools to disseminate new methods widely as well as using these new methods to address outstanding questions in human, animal and pathogen evolution.

The initial focus of the work will be to extend the StarBEAST2 package to allow for sampled ancestral species and their phenotypes in the species tree as well as ancient DNA samples in the embedded gene trees. This is a major software engineering task. There will also be work on developing new trait evolution models that can account for trait variation both within and between species. Current models will be incorporated into the BEAST 2 software and major studies on real and simulated data will be run to assess their strengths and weaknesses. The successful candidates will also have the opportunity to develop new inference methods for continuous trait evolution. Finally a third focus of the work will be on incorporating rich fossil data into the phylogenetic framework within BEAST 2. New models will incorporate variability of sampling over time and space, trait-dependent sampling, and will be able to use multiple fossils from the same morphospecies while accounting for uncertainty in the geologically-derived age of fossils. Both simulated and curated data sets will be used to test and prove the newly developed methods.

The successful candidates will work with an international team including Professor Alexei Drummond, Dr David Welch (University of Auckland), Associate Professor Tanja Stadler (ETH Zurich) and Dr Nick Matzke (ANU), as well as well as expert collaborators with knowledge of specific palaeontological and molecular data sets.

Each position comes with a stipend of $NZ27,300 (which is annually adjusted for inflation) and payment of enrolment fees.  There is no teaching requirement associated with the stipend.

The successful applicants will have a strong background in a quantitative subject (such as Computational Biology, Mathematics, Statistics, Computer Science, Physics or similar), an understanding of Bayesian statistics, some experience of coding and ideally have had some exposure to, or at least a strong interest in, phylogenetic methods. The exact nature of the work will depend on the strengths and background of the successful candidates.

For more information and to express interest please send your CV to Professor Alexei Drummond ( or Dr David Welch (



Predictive phylodynamic model of epidemic disease spread

Human networks are complex and data rich. The specific structure of New Zealand human networks determine where and how fast new diseases spread once they arrive from overseas sources. In particular, the structure of social contact networks and human transport systems are major determinants of the outcome of new epidemics. A striking example and repeated natural experiment is the arrival to New Zealand shores each year of influenza and other winter-only seasonal illnesses. Every year multiple types and subtypes of human influenza virus (H1N1 A, H3N2 A, B) arrive in New Zealand at the beginning of the winter season. These seasonal epidemics are established from a small number of infected individuals arriving early in the season and the epidemic subsequently spreads through local transmission via human-to-human contact and proximity, following human networks.

We propose to produce an accurate empirical seasonal model of human contact and movement through the use of anonymised and aggregated mobile phone data and other data sources. We will use this model to analyse full genome sequencing of influenza viruses from a country-wide stratified sample of influenza isolates over three seasons. Phylodynamic models that can analyse such high resolution genomic data obtained from a geographically representative sample of infections can be used to estimate fundamental epidemiological parameters, track the geographical spread of the disease, and determine the statistical regularities shared by seasonal outbreaks. Through joint analysis, prediction of the probability of invasion of local areas at different time horizons in future seasons may also be possible.

Three-year outcome: A national empirical model of human movement at DHB, census-block and mesh-block level for an acute airborne infectious disease spreading through New Zealand, along with new phylodynamic tools to integrate this model into the analysis of influenza genome sequences, and an analysis of such data.

Applicants for this scholarship should hold an honours or masters degree in bioinformatics, evolutionary biology, computer science, statistics or related fields.

The scholarship is tenable for three years, it will pay for tuition fees (for a domestic or international student) and a stipend of $25,000 per annum.

For more information contact Professor Alexei Drummond

Genetic coding

Stepwise evolution of genetic coding.

We would like to simulate the stepwise emergence of genetic coding in a GRT system, resembling the pre-LUCA branching of the joint Class I and II aaRS family trees.

The first gene.

How well can we define the first nucleotide sequences that stored genetic information?  We will use novel techniques of pre-LUCA ancestral reconstruction of aaRS sequences to find genes whose complementary strands may have crudely encoded separate Class I and II codon assignments.  

For further information, please contact Associate Professor Peter Wills