PhD studentship in information extraction from the published scientific research literature

Overview

The University of Melbourne is recruiting applicants for a 3-year PhD studentship in information extraction from the published scientific research literature. Topics of interest include relation and event extraction, co-reference resolution, and analysis of the semantics of tables. The precise focus of the PhD research will be determined based on the student’s interests, but is expected to involve natural language processing of the scientific literature, with a particular desire to develop novel approaches to biochemical information extraction that take advantage of deep learning. The project is in collaboration with Elsevier BV, a leading scientific publisher. In addition to access to substantial full text literature that is enabled through this collaboration, we will be working closely with the Content and Innovation team at Elsevier to develop solutions that have substantial practical applications.

The student will join the natural language processing group in the Department of Computing and Information Systems at the University of Melbourne and will work under the direction of Associate Professor Karin Verspoor and Dr Trevor Cohn. The project also involves collaboration with Associate Professor Lawrence Cavedon from RMIT University. The University of Melbourne is an internationally recognised research-intensive university, rated as being well above world standard in the area of Information and Computing Sciences (ERA 2015). The Department was ranked 13th in the QS World University Rankings for Computer Science and Information Systems (2015). The NLP group includes four permanent academics, and a substantial group of postdoctoral researchers and doctoral students, and has a strong presence in the computational linguistics community and beyond.

Company:

University of Melbourne

Qualifications:

The ideal applicant would have:
* a strong undergraduate honours (with research) degree or a Masters Degree in Computer Science or related field
* understanding of methods in machine learning and computational linguistics
* interest in acquiring knowledge of the biochemical domain, if not some prior experience with the domains of biology or chemistry
* prior experience in research projects
* good communication and writing skills

Specific requirements:

The student would ideally begin in July 2016 for Semester 2 2016, although that is dependent on the University application and scholarship processes and confirmation of the external funding. The scholarships are available to both domestic Australian and international students.

Educational level:

Master Degree

Tagged as: Academia, Australia, Computational Linguistics, Machine Learning, Master Degree, Natural Language Processing, NLP