3 Fully Funded PhD Scholarships in Health Data Science at UQ

Fully funded PhD scholarships are available at the Data Science group, University of Queensland for the following three projects (for start in April or July 2019). Application deadline 15/02/2019.

Project 1: Medical Knowledge Graph Extraction

This project aims to explore automated processes to learn high quality Knowledge Graphs from various medical data including textbooks, journals, trusted web content, and electronic medical records, principally in Chinese. Natural language processing and statistical analysis will be applied to extract medical entities such as symptoms, diseases, drugs, etc. from unstructured and semi-structured data, as well as correlation and causation between these entities. This will be the first step towards developing learning models that perform diagnostic inference directly on top of the automatically built health knowledge graphs.

Successful applicants will have a bachelor degree (with honours – or equivalent degree, including Masters) in Computer Science or related field; solid programming and algorithmic skills. Preferred, but not essential: knowledge of Information Retrieval, Natural Language Processing, Machine Learning, demonstrated by relevant experience, courses or publications.

To be considered for this scholarship, please email the following documents to Dr Wen Hua ([email protected]) and Dr Guido Zuccon ([email protected])

• Cover letter, which should also highlight why you are interested in this project, and any previous relevant experience
• CV
• Academic transcript/s
• Any copy of previously published research paper where you are an author, along with a statement regarding your contribution to the research paper.

Please note the following: Submitting the above documents does not constitute a full application for admission into The University of Queensland’s PhD program.

Project 2: Medical Image Analysis

This project aims to develop a unified framework to automate manual diseases diagnosis by addressing key research challenges in medical image analysis:

1) improving the quality and speed of medical image annotations;

2) transferring knowledge of different imaging procedures;

3) building medical imaging datasets using active learning; and

4) providing accurate segmentation/localization of objects/areas-of-interest boundaries in medical images.

Successful applicants will have a bachelor 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 (Torch, Tensorflow, Theano, Caffe, etc.); in-depth knowledge of computer vision methods and algorithms.

To be considered for this scholarship, please email the following documents to Dr Mahsa Baktashmotlagh ([email protected]) and Dr Guido Zuccon ([email protected])

• Cover letter, which should also highlight why you are interested in this project, and any previous relevant experience
• CV
• Academic transcript/s
• Any copy of previously published research paper where you are an author, along with a statement regarding your contribution to the research paper.

Please note the following: Submitting the above documents does not constitute a full application for admission into The University of Queensland’s PhD program.

Project 3: Human-in-loop Medical Data Quality Management

This project aims to design and experimentally evaluate novel human-in-the-loop techniques to support and improve the results of data-driven algorithms on data in Chinese. For example, you will investigate:

1) the use of human feedback to improve the quality of automatic medical entity relationship extraction and knowledge graph construction;

2) improving the use of manual annotations to support AI methods for medical image processing (also in combination with active learning);

3) designing novel task routing algorithms to assign a manual data labelling task to the right medical expert, considering at the same time (a) the cost to access experts, (b) the skills required to complete the task, and (c) the task priority; and

4) task design strategies that can minimise the time required by medical experts to complete the task effectively.

Successful applicants will have a bachelor degree (with honours – or equivalent degree, including Masters) in Computer Science or related field; solid programming and algorithmic skills. Preferred, but not essential: knowledge of Information Retrieval, Natural Language Processing, Machine Learning, demonstrated by relevant experience, courses or publications.

To be considered for this scholarship, please email the following documents to Dr Gianluca Demartini ([email protected]) and Dr Guido Zuccon ([email protected])

• Cover letter, which should also highlight why you are interested in this project, and any previous relevant experience
• CV
• Academic transcript/s
• Any copy of previously published research paper where you are an author, along with a statement regarding your contribution to the research paper.

Please note the following: Submitting the above documents does not constitute a full application for admission into The University of Queensland’s PhD program.

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These projects will be part of the new University of Queensland’s Health Data Science research lab created in collaboration with industry partners. The new research lab is part of the Data Science Research Group (https://www.itee.uq.edu.au/research/data-science), Information Technology and Electrical Engineering School, at the University of Queensland located in Brisbane, Australia.

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 4 full professors, two DECRA fellows and an Advanced Queensland Fellow), 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, VLDB, ICDE, ICDM, KDD, 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.

Expected start for all projects: April or July 2019

POSITIONS NOW FILLED