PhD Studentships in network science and automated, scalable and adaptive predictive modelling - [Archived Advertisement]

Advanced Analytics Institute (AAi), Faculty of Engineering and IT, University of Technology Sydney, Australia

Applications are invited for FOUR 3 year, fully funded PhD research studentships to work on the following projects:

• Automatic composition, optimisation and adaptation of multi-component predictive systems
• Development of robust and scalable hyperbox based machine learning algorithms
• Adaptive approaches for predictive modelling of complex multilayered networks
• Multiple spreading processes in real-world dynamic complex networks

The students will be joining the Advanced Analytics Institute in Sydney and will also have an outstanding opportunity to gain a diverse experience of both academic and commercial environments for which the AAi is very well known.

The studentship carries a basic remuneration of $27,082 pa tax-free and a waiver of the full-time research student fee. There are no restrictions on the nationality of the applicants and the selection will be based on the candidate’s qualifications and experience.

Applicants should have a very strong mathematical and computational background and hold a good Bachelor or Master’s degree in computer science, mathematics, physics, engineering, statistics or a similar discipline. Additionally the candidate should have very strong programming skills and experience using any or ideally a combination of Java, C++, Python, R and Matlab. Knowledge of and exposure to the big data platforms and technologies will be an advantage.

Before the formal application please contact the relevant supervisor listed in the description of the individual projects to discuss your suitability. Further PhD subject  relevant information can be found on the following supervisors’ www pages: http://bogdan-gabrys.com and http://katarzyna-musial.com.

HOW TO APPLY: Interested candidates should follow the application procedure listed on the University of Technology Sydney’s web pages: https://www.uts.edu.au/research-and-teaching/research-degrees/applying-uts/how-apply and apply following this link: https://msa.uts.edu.au/eStudent/S1/eApplications/eAppLogin.aspx?f=UTS.WAP.LOGIN.WEB.