PhD Scholarships in Computer Science and IT, RMIT University, Australia

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RMIT University AustraliaThe School of Computer Science and IT is offering twelve PhD scholarships to high achieving international and domestic students for various areas of research.

This School is internationally recognized for its research strength and is ranked in the top eight Australian universities for Computer Science and Information Systems (according to QS World University Rankings).

Value and duration
There are twelve scholarships available, valued at up to $25,000 per annum for three years (no extension possible). International candidates may be eligible for a tuition waiver.

To be eligible for this scholarship you must:

  • have an honours/master degree in the area of Computer Science or related disciplines
  • meet RMIT’s PhD entry requirements or
  • be studying a PhD in RMIT’s School of Computer Science at RMIT in Melbourne.

How to apply
To apply for one of these scholarships, you must complete an application consisting of:

  • a one page research proposal outlining your interest and suitability for the above projects
  • a detailed curriculum vitae
  • undergraduate and honours/master transcripts
  • contact details of two referees.

Submit your application via email directly to Associate Professor Xiaodong Li (

Close date
Applications close 31 October 2013.

Further information
Special emphasis is being put in areas where RMIT has a leading edge and a critical mass of researchers: namely Life Sciences (Health/Bio) and Disaster Management. We are looking for top students to work in collaborative projects with researchers at the NICTA Victoria Research Lab. Scholarships are funded by NICTA.

Applicants are encouraged to apply for Australian Postgraduate Awards or International Postgraduate Research Scholarships. Top-up scholarships may be available to successful recipients.

The twelve PhD scholarships available for PhD research are in the following areas:

1. Web service design for transportation networks: This project involves design of service-oriented management infrastructure for managing the databases and applications of the multimodal transportation networks. Web services act typically as the building blocks for Service-Oriented Architectures (SOA). Those building blocks need to be designed in an efficient and effective manner to provide a competitive service. During this process, it is important to investigate the design issues related to granularity, abstraction, generality, etc. Similarly, it is also important to evaluate the functional, non-functional and behavioral properties of web services to produce an optimum design. Therefore, developing a rigorous foundation that would provide sound design is paramount. It will help to reduce the effort of integration, minimize the impact of change, deliver appropriate levels of granularity, abstraction and generality, etc. In addition to RMIT’s PhD entry requirements, candidates should have background in a Computer Science or related discipline.

2. Multi-objective optimisation and data mining for journey planning: Multi-objective optimisation has proven to be successful in solving a wide range of real-world optimisation problems. This project will investigate a multi-objective and evolutionary computing approach to journey planning on real-world data for a metropolitan transportation network. The project will aim to develop a journal planner that will produce tailor-made solutions based on preferences information collected from passengers. In addition to RMIT’s PhD entry requirements, candidates should have strong artificial intelligence, machine learning, data mining, and optimisation background. Previous research experience in evolutionary multiobjective optimisation (evidence of peer-reviewed publications) is highly desirable.

3. Engineering optimisation with Swarm Intelligence techniques: This project involves developing and improving multiobjective particle swarm optimisation (PSO) algorithms for challenging real-world engineering optimisation problems. More specifically, the project will provide solutions to optimising design parameters related to a composite fabrication process. Design solutions will be evaluated using a CFD (Computational Fluid Dynamics) model. This project will be carried out in collaboration with the aerospace engineering school. In addition to RMIT’s PhD entry requirements, candidates should have a strong background in Computer Science or engineering discipline.

4. Machine learning and data mining applied to theme park game-play experience: This project will investigate interesting areas at the intersection of machine learning, games design of virtual-physical play and human-computer interaction. It will delve deep into how can artificial intelligence for computer games be used to produce a big leap in the state-of-the-art for theme park attractions: it will do so by learning from demonstrations of real players that can then be used to iteratively refine the design of sophisticated virtual-physical play, a notion that exceeds current definitions of augmented or virtual reality. The PhD candidate for such a project will need to have a strong command of machine learning and AI (for computer games), an inclination towards games design and the willingness to work on problems with humans-in-the-loop. Moreover, strong fluency in games and/or graphics programming is necessary i.e., C/C++, scripting languages for games and some high-level games APIs such as Unity3D, Unreal engine, etc. This PhD scholarship will be available only to Australian citizen or permanent resident.

5. Answering Real-time Questions from Arabic Social Media: Systems designed to help users answer questions have traditionally focused on analyzing formal content (e.g., full web pages and news articles) to find answers (or nuggets) to asked free-text questions. However, little attention has been given in building those systems to analyzing online informal content (such as millions of posts and tweets that are created daily on Facebook and Twitter). The rapid increase of popularity and interest in that type of media, especially in the Arab world, as both conversational and information dissemination channels, makes it a potential rich source of answers to real-time questions. In this proposal, we plan to address the problem of answering users’ questions from Arabic content in social media. While the type of data allows new user-centric questions to be asked (e.g., what are the different opinions on a decision made by a national figure right after it was made), it also opens up new challenges, such as dealing with different dialects, mixed languages, and conversational content, in addition to the unique characteristics of the Arabic language. We propose to explore the solution space from several different perspectives, e.g., ranked retrieval, topic modeling, and information visualization. In solving the problem, we plan to build a scalable open-source real-time system that answers given questions while providing plausible explanations of selecting the presented answers. Pre-requisites: Experience with search, retrieval, and/or summarisation of text would be preferred. Ability to read and write Arabic, also valuable.

6. TRIIBE: Tracking indoor information behaviour: Supplying the individual information needs of online users is well understood but a new frontier is on the horizon. It is the servicing of information seekers in large indoor areas such as museums, corporate headquarters, airports, shopping malls, and university buildings. Here activities in the space drive and define demands for data and this is a new challenging area of research. Accurate information provision requires tracking of visitors that is both privacy preserving and practical. Using the unexplored approach of passively tracking the Wi-Fi signals of mobile devices we will create a system that can acquire, synthesize and derive location information to support indoor space management and deliver personalised content to users. Pre-requisites: Experience with search, retrieval, and/or spatio-temporal analysis of data.

7. Sub-collection retrieval: understanding and improving search engines: Modern search engines need to find useful answers from vast collections of diverse documents. Currently, a single ranking function is used to identify candidate answers. However, our recent pilot work has shown that using different ranking approaches for different parts of a document collection has the potential to significantly boost search performance. This project will analyse different definitions of sub-collections, and study which features of ranking functions lead to different performance on distinct types of documents. This new knowledge will lead to a deeper understanding of search systems, and be used to create new ranking approaches, substantially improving on current search techniques, and benefitting all users of such tools. Pre-requisites: Experience with search, retrieval, and/or evaluation of IR systems.

8. Big data analytics: Big data is a focus of interest, both in academia and the industry. Most people characterize big data as data with the following properties:

  • Big volume: The size of the database is too large to manage with current tools.
  • Big velocity: The data is arriving too fast for systems to handle.
  • Big variety: The data is coming from too many disparate, heterogeneous sources.

Special emphasis is being put in areas where RMIT has a leading edge and a critical mass of researchers: namely Life Sciences (Health/Bio) and Disaster Management. We are looking for top students to work in collaborative projects with researchers at the NICTA Victoria Research Lab. Scholarships are funded by NICTA.

9. Big data medical analytics: The project involves data mining of the medical records of a very large public hospital. The work could involve medical images. Preference will be given to someone who has a first class Honours or equivalent, and could start as soon as possible. The project best suits an Australian citizen or permanent resident.

10. Crowd sourcing and data mining: Crowdsourcing is an activity designed to outsource a task to the crowd. An example of crowdsourcing is crowd-voting which gathers public opinion and feedback on certain matters. This project will investigate effective techniques for integrating crowdsourced data regarding public transport with existing repositories and sensors, to generate real-time information about transportation networks, e.g., late arrivals of trains, road accidents causing delays for trams and/or hazards for bike riders and commuters within Melbourne. Opportunities exist for modelling and visualisation of large amounts of realtime live transportation data, for the development of web services and apps for commuters and research into improved timetables, as well as optimised connection and prediction opportunities for new enhanced travelling routes. We are looking for PhD candidates with an excellent computer science background, as well as strong visualisation and communication skills.

11. Modelling and Understanding Human Behaviour for Simulation: This project involves exploring how to represent human behaviour in simulation systems, and how that behaviour interacts with other aspects of the simulation. The focus is on agent based modelling and simulation systems, with Emergency Management as the application area. The project is in collaboration with the NICTA constraint based optimisation group, and will involve investigation of how to integrate human behaviours into a larger simulation system. Questions involve representation of human behaviours (building on Belief Desire Intention models), processes for eliciting and validating behavioural models, understanding of behaviours and their impact during execution, controlling and analysing behavioural details and their overall effects, technical integration of human behaviours with other aspects of a simulation, etc. Candidates should have strong analytical skills and a solid background in computer science, as well as an interest in inter-disciplinary research. Contact for further details.

12. Agent based simulation for sustainable urban growth: This project is part of an inter-disciplinary industry collaboration, funded by a competitive ARC Linkage project. The candidate will be part of an interdisciplinary team involving computer scientists, social scientists and urban planners. The overarching project will use social science data on home buyer behaviour to model in detail the behaviour of home buyers, within a simulation that attempts to model the evolution of the city and its neighbourhoods over decades. The behaviour of other stakeholders such as developers, councils, etc. will also need to be modelled, though in less depth than home buyers, with an aim to understand how certain schemes or policies may influence urban development, given the in-depth understanding of buyer behaviour. The focus of the PhD could take a number of directions within the context of this project. These may include integration of (overlapping and independent) simulation modules, modelling of human behaviour in ways that map to social science theories or sensitivity testing of behavioural aspects of a simulation. Other options can also be explored. Contact for further details.

For further information, contact Associate Professor Xiaodong Li

Disclaimer: Every effort has been made to ensure the above information is current and correct. However, applicants should contact the appropriate administering body before making an application, as details do change frequently.


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