Australia-Israel PhD Research Scholarship (co-funded by Swinburne and the Trawalla Foundation)
Each candidate will receive:
• A full tuition fee scholarship for 4 years
• A stipend of $30,000 p.a. for 3 years
• Funding towards international air fare travel to Tel Aviv University to complete part of candidature
The 'Australia-Israel PhD Research Scholarship' program aims at strengthening relations between Australia and Israel, by fostering innovation and academic exchange. The scheme will support a total of two Partnered PhD candidates starting their PhD in 2017.
Eligibility / selection criteria
To become and remain eligible for the Australia-Israel PhD research scholarship, a prospective candidate must:
• Have completed at least four years (or equivalent) of tertiary education studies in computer science, computer engineering or related discipline at a high level of achievement (75% or higher).
• Have good understanding of data management; some knowledge of statistics is preferred.
• Fulfill the PhD candidature entry requirements of both Swinburne University of Technology and Tel Aviv University including language proficiency.
• Not have previously held a postgraduate research scholarship from Swinburne University of Technology
Successful applicants will be required to:
• Enroll as a full time student in the PhD program of Swinburne University of Technology
• Spend 12 months at Tel Aviv University under joint supervision of Tel Aviv University and Swinburne University of Technology academics
A stipend of $30,000 p.a. for 3 years.
How to apply
The applicants will work in one of the PhD projects outlined below:
• Digital Humanities: Digital Humanities is an emerging, interdisciplinary area of research which looks to enhance and redefine traditional humanities scholarship through digital means. The ability to scan huge volumes of material, to search specific data and establish connectivity between different bodies of knowledge by either connecting metadata from several institutions sometimes using semantic linking mechanisms (powered by SKOS or RDF), or by culling statistical data in order to acquire quantitative results, has turned the benefits of digital technologies from being just "work and time savers" to tools that bring about significant qualitative results, and opening up new fields of research and thought.
• Deep Learning for Question-Answer Systems: Assembling useful Question-Answer (QA) repositories out of operational systems (eg. Customer care lines) is a very hot area. The QA pairs may come from different types of resources, i.e. forums and social media discussions, email inquiries, and customer service recordings & documents. Furthermore, different types of resources may use different terminology/expressions to depict the same question and/or answer. Deep learning is a powerful technique not relying on hand-crafted features (which are less data-driven in terms of feature representation) that other methods use. Deep learning is also very good at knowledge transfer. For example, a QA deep network modeled on email inquiries may be learnt together with other QA deep networks modeled on other resources such as customer service recordings & documents under a multi-task transfer learning framework.
• Analysing product development, manufacturing systems, and business models data to improve Industry 4.0 platforms: Industry 4.0 solutions bring together cyber, physical and human systems that work synergistically towards new manufacturing platforms. These systems generate their own data captured typically via IoT and Cloud infrastructures. Analytics becomes very important in this context as it is the primary means of extracting insights, forecasting and suggesting alternatives. The project will focus on building a Data Incubator (DI) for Industry 4.0. A DI is a data platform that links industry, government, and research, by efficiently leveraging the potential of Big Data, through access and deployment of the appropriate resources and available tools. The DI can provide an isolated environment for industry to incubate and commercialise innovative data-driven solutions. It will provide best of- breed technology in Big Data ingestion, management and analysis, predictive analytics, and data visualisation and will accelerate innovation for Industry 4.0. Example analytics solutions that will be studied include: 1) Determining patterns in major and minor stoppages in production lines. Studying if major and minor stoppages can be predicted with a high level of accuracy using collected data; 2) Complex flaw detection and prediction; correlating data across machines and processes; and 3) Real-time IoT data analysis; synthesizing data with different time, scale, noise, uncertainty characteristics.
• Graph Streaming Data Analytics: Streaming graph data are very common nowadays especially due to the emergence of social network platforms. Graph streaming algorithms have emerged as a paradigm for analyzing massive datasets in which there are limited memory resources available. Many graph problems require novel solutions to be devised in a streaming environment. For example, dense subgraphs indicate interesting structures in many real-world graphs, including social networks, web-link graphs, and biological networks. The Densest Subgraph problem has emerged as a key computational question in datasets best modeled as graphs. Another commonly found example is community detection, where communities of nodes with particular characteristics are sought. This project aims at studying new streaming algorithms for a variety of known graph problems, with special emphasis on the effectiveness, efficiency and scalability of algorithms.
To express interest for these positions, please send by email to firstname.lastname@example.org –
• An current CV
• Transcripts of all previous qualifications (BSc, MSc, etc)
• A research proposal on one of the above projects, up to 5 A4 pages, describing the research questions and general methodology in addressing them. You can view the above areas as general areas of research and you can suggest specific research topics that you find interesting.