This project is closed for international students.
Project summary
- Program
- PhD
- Location
- St Lucia
- Research area
- Information and computing sciences
Project description
Improve safety, transferability, and robustness in deep learning models. This project will be part the Data Science Research Group, Information Technology and Electrical Engineering School, at the University of Queensland located in Brisbane, Australia.
Research environment
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: Data and knowledge engineering, Information Retrieval, Computer Vision, and Complex and Intelligent Systems. You will join a world-leading research group currently composed of 13 academic staff members (including 6 full professors, two DECRA fellows and an 2 Future Fellows), 7 research fellows and over 40 PhD students. Members of the group have a successful track record of publishing in top conferences and journals such as ACM SIGIR, ACM CIKM, The Web Conference (WWW), SIGMOD, CVPR, ICCV, ICML, PAMI, JMLR, ICLR and various ACM and IEEE transactions.
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. Brisbane is a liveable, capital city with great weather, vibrant green spaces, lively bars and restaurants, world-class art galleries and premier events. It is the third most populous city in Australia and is closed to premier recreational locations such as the Sunshine Coast and the Gold Coast.
Scholarship
This is an Earmarked scholarship project that aligns with a recently awarded Australian Government grant.
The scholarship includes:
- living stipend of $35,000 per annum tax free (2024 rate), indexed annually
- your tuition fees covered
- single overseas student health cover (OSHC).
Learn more about the Earmarked scholarship.
Supervisor
Principal supervisor
You must contact the principal supervisor for this project to discuss your interest. You should only complete the online application after you have reached agreement on supervision.
Always make sure you are approaching your potential supervisor in a professional way. We have provided some guidelines for you on how to contact a supervisor.
Preferred educational background
Your application will be assessed on a competitive basis taking into account your:
- academic record
- publication record
- honours and awards
- employment history.
Successful applicants will have a bachelor's degree (with honours – or equivalent degree, including Masters) in Computer Science or Electrical Engineering or Biomedical Engineering or Physics, or a related field; solid programming and algorithmic skills. Preferred, but not essential: in-depth knowledge of machine learning techniques, particularly deep learning demonstrated by relevant experience, courses, or publications; hands-on experience with one or more deep learning libraries (PyTorch, Tensorflow, etc.); in-depth knowledge of computer vision methods and algorithms.
You'll demonstrate academic achievement in Computer Vision and Machine Learning and the potential for scholastic success.
A background or knowledge of Deep Learning Frameworks is highly desirable.
How to apply
This project requires candidates to commence no later than Research Quarter 4, 2024. To allow time for your application to be processed, we recommend applying no later than 30 June, 2024 31 March, 2024.
You can start in an earlier research quarter. See application dates.
Before you apply
- Check your eligibility for the Doctor of Philosophy (PhD).
- Prepare your documentation.
- Contact Dr Mahsa Baktashmotlagh (m.baktashmotlagh@uq.edu.au) to discuss your interest and suitability.
When you apply
You apply for this scholarship when you submit an application for a PhD. You don’t need to submit a separate scholarship application.
In your application ensure that under the ‘Scholarships and collaborative study’ section you select:
- My higher degree is not collaborative
- I am applying for, or have been awarded a scholarship or sponsorship
- UQ Earmarked Scholarship type.