Summary
| Enrolment status | New students |
|---|---|
| Student type | Domestic students, International students |
| Level of study | Higher Degree by Research |
| Study area | Engineering and Computing |
| HDR funding type | Living stipend scholarship |
| Scholarship value | $28,597 per annum (2021 rate), indexed annually |
| Scholarship duration | Three years with the possibility of two 6-month extensions in approved circumstances |
| Opening date | 28 January 2021 |
| Closing date | 14 February 2021 |
Description
Supervisor - Dr Wen Hua
This project aims to establish a methodology for spatiotemporal entity linking by utilising object movement traces to support database integration and data quality management for the next-generation of data where spatiotemporal attributes are ubiquitous. It expects to develop a novel entity linking paradigm for automatic, efficient and reliable spatiotemporal data integration together with a new data privacy study in this context. Expected outcome include new database technologies for data signature generation and similarity-based search, and improved location data privacy protection methods. This project should provide significant benefits to all areas where high quality spatiotemporal data fusion is essential to meaningful data analysis.
The student will be joining the UQ Data Science research group to work on this project. UQ DS is a world-leading group in the areas of information systems and data management. It aims to find innovative and practical solutions for creating value from big data in business, scientific and social applications. Members in the DS group have an enviable record of securing competitive funding from ARC and industry, as well as a stellar record of publication at the top-ranked conferences and journals. The student can also benefit from the data-intensive computing infrastructure in the DS group that is capable to support terabyte level high performance storage and processing.
A working knowledge of database techniques, spatiotemporal data analysis and Java/C++ programming would be of benefit to someone working on this project.
Eligibility
To be eligible, you must meet the entry requirements for a higher degree by research.
This scholarship is open to Australian citizens, permanent residents and International students who are currently in Australia at the time of application and commencement.
Before you get started
If this scholarship has rules, download and read them.
How to apply
To be considered for this scholarship, please email the following documents to Dr Wen Hua (w.hua@uq.edu.au)
- Cover letter
- CV
- Academic transcript/s
- Evidence for meeting UQ's English language proficiency requirements eg TOEFL, IELTS
Please note the following: Submitting the above documents does not constitute a full application for admission into The University of Queensland's PhD program. If you are selected as the preferred applicant, you will then be invited to submit a full application for admission. You can familiarise yourself with the documents required for this process on the Future Student's website.
Selection criteria
Applications will be judged on a competitive basis taking into account the applicant’s previous academic record, publication record, honours and awards, and employment history.
The applicant will demonstrate academic achievement in the field/s of inforamton systems and data management and the potential for scholastic success.
A background or knowledge of data structure and algorithms, data mining and machine learning is highly desirable.
Contact
Terms and conditions
Read the policy on UQ Research Scholarships.
A domestic part-time student with carer’s responsibilities, a medical condition or a disability, which prevents them from studying full time may be eligible for scholarship consideration, on a case by case basis.