PhD Scholarship in Computer Vision & Machine Learning

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 $27,596 per annum tax-free (2019 rate), indexed annually. Additional top-ups will be available for outstanding candidates.
Scholarship duration Three years with the possibility of two 6-month extensions in approved circumstances.
Opening date 10 July 2019
Closing date 31 August 2019

Scholarship description

Outstanding and motivated PhD candidates, with very good first Masters or honours degree in mathematics, computer science or science, are being sought to work as part of a research team on developing methods for solving a wide range of problems in computer vision and machine learning. A strong background in mathematics and some programming experience is desirable for this position. Experience in computer vision and/or machine learning is also meriting. The project is supported by the Australian Research Council and involves academic partners in both Europe and the US. This project is part of the Data Science Research Group (Information Technology and Electrical Engineering School) at the University of Queensland located in Brisbane, Australia.

The Data Science group researches and develops innovative and practical solutions for business, scientific and social applications in the realm of big data. The group encompasses a variety of research strengths including: Computer Vision & Machine Learning, Data and knowledge engineering, Information Retrieval, and Complex and Intelligent Systems. You will join a world-leading research group currently composed of 14 academic staff members (including 4 full professors, one Future Fellow, two DECRA fellows and an Advanced Queensland Fellow), 7 research fellows and over 40 PhD students.  Members of the group have a successful track record of publishing in top conferences and journals.

The research environment available to the project is world-class. The University of Queensland (UQ) has a strong and internationally focused research culture. It is ranked in the top 1% of world universities in three widely publicized international University rankings. The areas of research in these PhD projects have a strategic fit within UQ’s existing research strengths in Data Science.

Eligibility

To be eligible, you must meet the entry requirements for a higher degree by research.

Applications are closed.

Before you apply

If this scholarship has rules, download and read them before applying.

How to apply

To be considered for this scholarship, please email the following documents to Dr Wen Hua (w.hua@uq.edu.au) and Dr Guido Zuccon (g.zuccon@uq.edu.au

  • Cover letter, which should also highlight why you are interested in this project, and any previous relevant experience
  • CV
  • Academic transcript/s
  • Copies of published research papers where you are an author, along with a statement regarding your contribution to the paper.

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 Graduate School's website.

Selection criteria

The applicant will have:

  • a bachelor degree (with honours – or equivalent degree) in Computer Science or related field
  • solid programming and algorithmic skills

The following are desirable:

  • knowledge of Computer Vision, Machine Learning, Optimization and/or Mathematics demonstrated by relevant experience, courses or publications.
Applications are closed.

Contact

Dr Guido Zuccon
Applications are closed.

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.

Applications are closed.