Scholarship details
Study levels
Postgraduate, Research and Honours
Student type
Future students and Current students
Study area
Data science, Health, Mathematics, Science, Science, technology and engineering and mathematics
Eligibility criteria
Indigenous Australian, Academic performance, Struggling financially, From regional, rural or remote areas, Sporting excellence, Women and From a non-English speaking background
Citizenship
Australian or New Zealand
Application dates
- Applications close
- 30 September 2024
What you'll receive
- You will receive a scholarship of $10,000 for one year, usually paid over two semesters.
- As the scholarship recipient, you will have the opportunity to work with a team of leading researchers, to undertake your own innovative research in and across the field.
- Honours/Master degree students will receive $5,000 in allowances (training, travel, thesis).
Eligibility
- You need to meet the entry requirements for a QUT degree in either an honours course or a Master of Philosophy, including any English language requirements.
- Enrol as a full-time, internal student (unless approval for part-time and/or external study is obtained).
- You must be an Australian or New Zealand citizen, Australian permanent resident, or a person entitled to stay in Australia, or enter and stay in Australia, without any limitation as to time.
How to apply
If you are (will be) a graduate (recently or otherwise) from any discipline, complete an expression of interest (EOI). The steps are:
- Complete the EOI available at Next Generation Graduates Program (NGGP): Sports Data Science & AI - Centre for Data Science (qut.edu.au)
- Peruse the projects on offer at Next Generation Graduates Program (NGGP): Our Projects - Centre for Data Science (qut.edu.au). Those that have already been awarded have a student name listed against them.
- Email your top three project preferences, along with your CV and academic record, to admin.sportsdata@qut.edu.au. We will be in touch with next steps.
About the scholarship
Prediction of relative hormonal environment for all common menstrual cycle ‘types’ (natural and pharmaceutically modified)
Themes
AI for holistic athlete performance and wellbeing / Personalised performance
Sports research objectives/questions
- Develop an open source predictive model for individual daily female hormone profile based on known variants, including both endogenous (natural cycle) and exogenous (medicated cycle) cycle types.
- Develop an AI model to understand the relationship between the predicted hormonal environment and daily tracking variables: physical/psychological/emotional.