Information Extraction from Large-scale Low-quality Data

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 – Dr Wen Huaw.hua@uq.edu.au

Information extraction, which identifies entities and relations from data, is a key technology that lays the foundation for understanding the semantics of data. This project aims to investigate the problem of information extraction by innovatively exploring the informality and temporal evolution of data. It expects to develop novel techniques for reliable, efficient, and scalable information discovery from large-scale low-quality data. Expected outcomes include a set of collective, contextualised, and temporal-aware algorithms for information extraction and integration, built on top of effective indexing and in-parallel processing. This project is anticipated to benefit a considerable number of data-driven intelligence-based applications.

We are seeking a full-time, highly motivated PhD candidate to perform the cutting-edge information extraction research. The successful applicant will work under the supervision of Dr Wen Hua (ARC DECRA Fellow), and as a team with researchers from UQ Data Science Discipline within the School of Information Technology and Electrical Engineering.

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 deep learning, natural language processing, Python and C/C++ would be of benefit to someone working on this project.

The applicant will demonstrate academic achievement in the field(s) of information systems and data management and the potential for scholastic success.

A background or knowledge of computer science or information technology is highly desirable.

*The successful candidate must commence by Research Quarter 1, 2022. You should apply at least 3 months prior to the research quarter commencement date. International applicants may need to apply much earlier for visa reasons.

Apply now