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Development of robust and scalable hyperbox based machine learning algorithms

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

The research conducted in this project will explore (fuzzy) interval, hyperbox or hyper-rectangle representation of the data as a basis for developing robust, scalable machine learning algorithms and evaluate their potential for reconfigurable hardware (for instance in FPGAs) and in-database implementations. The investigation will focus on exploiting a set of simple operations to create and adapt over time a potentially complex, multilevel prediction system. The project will build on previous work in Prof. Gabrys' group concerned with GFMM family of learning algorithms and method independent methodological aspects of building dynamically adapting predictive systems.

The student will be joining the Advanced Analytics Institute in Sydney and work primarily with Prof. Gabrys but 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.

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 as well as reconfigurable hardware including FPGAs will be an advantage.

Before the formal application please contact Prof Bogdan Gabrys, e-mail: to discuss your suitability. Further PhD subject relevant information can be found on the following www pages: http://bogdan-gabrys.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.

Funding Notes

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.

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