PhD scholarship Data Analysis Engine for Medical Discovery and Prevention, Swinburne University of Technology (Sarawak, Malaysia Campus)

Scholarship information 

Full tuition fee waiver for full-time PhD study (MYR31,195 per annum) plus an allowance of MYR22,800 per annum for 3.5 years subjected to satisfactory annual progress review and the University’s approval.

Closing date for applications: 

29 Feb 2016.

Interview via skype: 7-11 March 2016 

PhD study starting date: 18 April 2016 

*Opportunities 

• On top of the PhD candidature requirements, these are additional opportunities for these candidates. 
• Traveling Requirement Successful candidate will be required to visit Swinburne Sarawak’s industry partner together with your supervisor. 
• Successful candidate will be required to visit Swinburne Melbourne Campus subject to supervisor’s approval. 
• The airfare and accommodation fees will be covered on top of the scholarship. 

Project Supervision Successful candidate will be supervised by research teams in both Swinburne Sarawak and Melbourne campuses.

Project: Data Analysis Engine for Medical Discovery and Prevention

This project will address an area of growing need within medical prevention, especially in the area of cardiology where there is a significant growth in the number of members of the public with cardiac diseases, but their medical condition often remains undiagnosed until it is too late for any preventive steps (e.g., special diet, exercise) to be undertaken. Using the historical data of patients with cardiac diseases, the main aim of the project is to develop a tool-set, associate methods and models that will allow for the identification of better early indicators of cardiac disease risks. In terms of the core research, the objective of the project is to develop and validate a Domain Specific Visual Language (DSVL) that can act as a bridge between the mental model of a medical practitioner, the underlying meta-model mined from the data in the domain, and the data processing pipeline that needs to be executed.

Preferred candidate’s background: 

i. Students with a Bachelor / Master degree in Computer Science, IT, Software Engineering or relevant Engineering disciplines can apply.
ii. Strong programming skills (students with no programming experience would not be considered)
iii. Knowledge of Machine Learning, and Data Mining would be an advantage
iv. Understanding of medical datasets would be an advantage v. Good written and oral communication skills in English are essential.
vi. Good analytical and critical thinking skills are essential.

Application Details Interested candidates should send a CV and latest grades by 8 Feb 2016 to Associate Professor Patrick Then (pthen@swinburne.edu.my).

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