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  PhD scholarship in Statistics and Data Science on Model-based Clustering for Mixed-type Data

   School of Mathematics and Statistics

   Applications accepted all year round  Funded PhD Project (Students Worldwide)

About the Project


This scholarship is for high-achieving students in Statistics and Data Science with an interest in pursuing a PhD researching model-based clustering methodology.


This PhD scholarship is funded by a Royal Society of New Zealand Marsden Fast-Start project. The successful applicant will be supervised by Dr. Louise McMillan at VUW and Dr. Emma Carroll in the School of Biological Sciences - Te Kura Mātauranga Koiora, University of Auckland - Waipapa Taumata Rau.

Clustering is a technique used in many applications, but few methods are available for clustering categorical data, or a mixture of categorical and numerical data, that do not simply pretend the categorical data are numerical. This project aims to develop unsupervised clustering methods for large datasets that include large numbers of categorical variables. Unsupervised methods do not require pre-labelled training data, so can be used even when the structure of the data is unknown.

The project will explore two different approaches, one based on Bayesian genetic clustering methods and the other entirely novel, and suitable for cases with high numbers of correlated variables. The main focus of the project will be development of new methodology and an accompanying R package to allow others to use the methodology.

The initial case study for the new methods will be animal conservation genetics, using existing genetic datasets from Dr. Carroll’s international collaborations. However, the algorithms developed will have potential applications in a huge range of research areas and industries, including medical surveys and commercial marketing.

Applicants are encouraged to contact Louise McMillan for further information about the project.

Who is eligible?

We are seeking a highly-motivated person with an excellent academic record, a good understanding of statistics and an interest in contributing to cutting-edge research relating to clustering and mixed-type data.

  • International or domestic students may apply.
  • Applicants must have completed a Masters degree in statistics or data science (or an Honours degree)
  • Applicants must have a minimum GPA of 8 (out of maximum GPA 9).
  • Applicants must satisfy the standard VUW PhD entry requirements, including English language proficiency.
  • Applicants must be proficient in the R programming language.

In addition to knowledge of statistics and R, experience in at least one of the following areas would be highly beneficial:

  • Clustering, especially model-based clustering
  • Genetics
  • Algorithm/methodology development
  • R package development
  • Simulations and modelling

The general PhD entry criteria for Victoria University of Wellington may be found here:

Selection criteria

Applicants will be selected on the basis of academic merit and any experience relevant to the research topic.

Application process

Applications should be sent to 

Scholarship specific documentation

  • Academic transcript
  • CV
  • Cover letter expressing your interest in the project
  • Contact details of two academic referees
  • A copy of any previous research theses may be requested

Selection process

Selection will be made by Louise McMillan, in consultation with the Statistics Postgraduate Coordinator.

Additional information

Applicants must be accepted into the VUW PhD programme (

The $35,000 per annum stipend for 3 years will be paid directly to the student in monthly payments. Tuition fees & levies will be paid directly to the University.

The student must complete PhD progress reports every 6 months to a satisfactory standard.

Acceptance of this scholarship include conditions similar to those of the Wellington Doctoral Scholarship (i.e. excepting those relating to the decision makers).


Applicants are encouraged to contact Louise McMillan for further information about the project at 

Biological Sciences (4) Computer Science (8) Mathematics (25)

Funding Notes

Funded via the Royal Society of New Zealand's Marsden Fund


Register your interest for this project

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