AI-driven Effective Query Formulation for Better Systematic Reviews

This Earmarked Scholarship project is aligned with a recently awarded Category 1 research grant. It offers you the opportunity to work with leading researchers and contribute to large projects of national significance.

Supervisor – Associate Professor Guido

This project aims to develop novel AI-based search engine methods to make the creation of systematic reviews cheaper, faster and unbiased. Systematic reviews are the cornerstone for evidence-based decisions in clinical practice and government policy making. Given the pace new research is published at, it is unsustainable to manually conduct systematic reviews in the traditional manner, taking on average 2 years and $350K and becoming already outdated when published. The outcomes of this project will lead to systematic reviews of higher quality, while reducing their financial and temporal costs, providing significant benefits to organisations performing reviews and their funders, and to people impacted by decisions made from the reviews.

Preferred educational background

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.

A working knowledge of information retrieval, natural language processing and transformer-based language models would be of benefit to someone working on this project.

The applicant will demonstrate academic achievement in the field(s) of information retrieval, computer programming, natural language processing, machine learning, deep learning and the potential for scholastic success.

*The successful candidate must commence by Research Quarter 3, 2022. You should apply at least 3 months prior to the research quarter commencement date.

Apply now