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Efficient Next-Generation Information Retrieval Systems

This scholarship is closed.

Enrolment status
Future UQ student
Student type
Domestic, International
Study level
Postgraduate research (HDR)
Study area
Computer science and IT
Scholarship focus
Academic excellence
Funding type
Living stipend, Tuition fees
Scholarship value
$29,863 per annum (2023 rate), indexed annually
Scholarship duration
3.5 years with the possibility of 1 extension in line with UQ and RTP Scholarship Policy
Number awarded
May vary
Applications open
24 August 2022
Applications close
26 September 2022

About this scholarship

Supervisor: Dr Joel Mackenzie

A funded PhD position is available in the School of Information Technology and Electrical Engineering (ITEE) at the University of Queensland (Brisbane, Australia) on next generation efficiency problems in Information Retrieval. At-scale search systems must provide effective results to an innumerable number of information needs, and they must do so extremely quickly, even when operating on collections containing many billions of documents with thousands of incoming queries each second. This results in a complex tension between the many aspects of efficiency (such as latency, throughput, resource consumption) and the utility of the system to end users. The aim of this PhD project is to develop and evaluate novel data structures and algorithms for enhancing the efficiency of "next generation" at-scale search systems, such as those which incorporate emerging deep learning technology. One particular example is to examine how modern deep learning approaches can be efficiently incorporated into large-scale search engines, and the best practices for doing so; on the other hand, there is also the opportunity to examine how deep learning methods (and other emerging technology) can be used to inform the design and implementation of efficient search systems. Although the project will operate in the context of core search system components (including indexing, storage, and query processing), there is scope to work on any aspects of the search pipeline, or in any particular domain of search.

A successful candidate is expected to have a good knowledge (and more importantly, an interest) in the fields of information retrieval, data structures and algorithms, or adjacent fields. Candidates should hold either an M.Sc. or a B.Sc. (honours) degree in computer science, have good English communication skills, and have strong programming skills in systems languages such as C, C++, or Rust. It is also desirable for candidates to have a good understanding of computing system architectures.

Research environment

The University of Queensland, Australia, is a top 50 university (47th in both the 2022 QS World University Rankings and in the 2022 Academic Ranking of World Universities). Embedded in the Data Science discipline in the School of Information Technology and Electrical Engineering (ITEE), you will be part of a world-leading group with expertise across several domains of computer science and beyond. You will be situated at the beautiful St Lucia campus next to the Brisbane river, about 7km from the Brisbane CBD.

Host for the 2032 Olympic Games, Brisbane is one of the fastest-growing capital cities in Australia in terms of population and employment. Brisbane residents are young and skilled, highly educated and culturally diverse. Brisbane has a lovely sub-tropical climate, with mild winters and warm humid summers. Even in the midst of a Brisbane winter you'll be yearning to play in the parks and dine outdoors. Brisbane is a green city with clean, healthy air and a clear commitment to its environment, with the Brisbane City Council maintaining carbon neutral status for its operations since 2017. Brisbane's public transport system is a clean and green network of trains, ferries (CityCats) and buses that have been integrated so commuters can travel seamlessly between each service. Brisbane's nightlife is extensive, spread across both sides of the River, and packed with variety and options.


You're eligible if you meet the entry requirements for a higher degree by research.

How to apply

Before submitting an application you should:

You apply for this scholarship when you submit an application for a Doctor of Philosophy (PhD). You don't need to submit a separate scholarship application.

When you apply, please ensure that under the scholarships and collaborative study section you:

  1. Select ‘My higher degree is not collaborative’
  2. Select 'I am applying for, or have been awarded a scholarship or sponsorship'.
  3. Select ‘Other’, then ‘Research Project Scholarship’ and type in ‘RETRIEVAL-MACKENZIE’ in the 'Name of scholarship' field.

Selection criteria

Your application will be assessed on a competitive basis.

We take into account your:

  • previous academic record
  • publication record
  • honours and awards
  • employment history

A working knowledge of systems programming in:

  • C, C++, Rust, etc,
  • scripting skills in Python, bash, awk, R, etc, and
  • knowledge of data structures and algorithms would be of benefit to someone working on this project.

You will demonstrate academic achievement in the field/s of information retrieval, data structures and algorithms, machine learning, natural language processing, and/or any relevant adjacent fields and the potential for scholastic success.

A background or knowledge of systems programming, data structures and algorithms is highly desirable.


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.