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).
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