PhD Scholarship Predictive Model for Oil Drilling, 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: Predictive Model for Oil Drilling 

Swinburne and a commercial software company have jointly ventured in developing software to crunch data from oil and gas borehole rigs. Data resulted from the oil rigs drilling processes has been collected in the past decade. This project focuses on working on a predictive model for more efficient drilling in future. In order to drill a borehole, driller (drilling company) has to balance time, cost, manpower and consumables in order to achieve adequate depth of drill with minimum time and resource. If time is too short, the drilling can be dangerous and more expensive. If time is too long, drilling can be less dangerous and in some cases more expensive or less expensive.

PhD candidate is required to design and implement a predictive model that can be used to estimate future drilling time and cost based on the ground parameters such as soil structure, depth etc.

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, Data Mining and Statistical Inference would be an advantage
• iv. Knowledge of Oil Drilling 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 29 Feb 2016 to Associate Professor Patrick Then (pthen@swinburne.edu.my).

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).