- Applications open
- 9 July 2021
- Applications close
- 30 June 2022
What you'll receive
- a living allowance valued at AU$30,000 per annum for three years.
This scholarship is for full-time study and can be used to support living costs. It includes provision for an additional 12 weeks paid sick leave and paid maternity leave.
- meet QUT academic and English language entry requirements for the IF49 Doctor of Philosophy (PhD)
- be able to commence the PhD course in 2022
- be an Australian citizen or permanent resident.
Applicants will be assessed against the Science and Engineering Faculty of domestic admission.
QUT and the Centre for Data Science are committed to equity and diversity among our staff and students, to ensure that we mirror the diversity of the community in which QUT exists.
Woman and Aboriginal and Torres Strait Islander students are encouraged to apply.
The provision of a scholarship is conditional on successful application and admission to the IF49 Doctor of Philosophy course. Eligibility for admission to a research degree is determined by the Graduate Research Centre.
How to apply
To be considered for this scholarship, you will need to apply for and be accepted into a PhD at QUT.
Indicate your interest in this scholarship by nominating Distinguished Professor Kerrie Mengersen as principal supervisor, and include the name of this scholarship in the financial details section.
Your expression of interest must include:
- a cover letter
- a summary (up to two pages) of your career outlining your experience in data science practice or research)
- contact details of three referees (full name, email, and phone number).
What happens next?
If your expression of interest is accepted, you'll be invited to submit a full application, including a research proposal, to finalise your application.
The scholarship will remain open until a suitable candidate is found. We are seeking to recruit a student as soon as possible.
The conditions for retaining the scholarship are set out in the rules of the QUT Postgraduate Research Award (Domestic), excluding the provision for extension.
About the scholarship
The Australian Bureau of Statistics and QUT Centre for Data Science are partners in a world leading program of research in data science in the government statistics domain and in the priority areas of agriculture and geospatial statistics. The Australian Bureau of Statistics is Australia’s national statistical agency providing trusted official statistics on a wide range of economic, social, population and environmental matters. The QUT Centre for Data Science’s vision is to be a national and global leader in the development of frontier methods for the use of data to benefit our world.
What’s in it for you?
- Opportunity to work closely with the Australian Bureau of Statistics.
- Opportunity to work with leading researchers in data science.
- Access to large datasets.
- Access to a community of like-minded data science researchers and practitioners.
Research Project One
Functional areas represent an area that to the maximum extent possible includes the geographic extent of a populations everyday activities like work, shopping, schooling and leisure. This is very similar to the idea of a labour market area which is an area that as far as possible includes a populations home and work locations.
Functional areas are ideal for the analysis of socio-economic statistics about a population as they combine the business and labour demand information in the same region as the demographic information about the consumers and workforce population that interact with those businesses. Current boundaries for geographic areas that represent functional areas are ripe for review.
This PhD research project will focus on enhancing the historical ABS process through solving this optimised clustering problem using known and novel data sources to inform an algorithm that can evolve to include additional datasets as they present themselves.
- Distinguished Professor Kerrie Mengersen.
Research Project Two
Statistics from combined sources using agriculture case studies. This includes the development of a production process to combine survey data with alternative data sources using deep neural nets, delivering small area outputs and enabling a transition from survey data to administrative inputs.
- Distinguished Professor Kerrie Mengersen.
For further information, contact the Director, QUT Centre for Data Science, Distinguished Professor Kerrie Mengersen.