Research Associate in Electrical Engineering or Computer Science

University of New South Wales School of Engineering and Information Technology


Research Associate

Apply now Job no: 501000
Work type: Full-time
Location: Canberra, ACT
Categories: Post doctoral research fellow

UNSW Canberra is a campus of the University of New South Wales located at the Australian Defence Force Academy in Canberra. UNSW Canberra endeavours to offer staff a rewarding experience and offers many opportunities and attractive benefits, including:

  •      One of Australia’s leading research and teaching universities
  •      Australian bush land setting with FREE parking 
  •      Strong commitment to staff development and learning
  •      Strong commitment to work life and family balance
  •      Generous superannuation opportunities

At UNSW, we pride ourselves on being a workplace where the best people come to do their best work

The School of Engineering and Information Technology (SEIT) is the largest school at UNSW Canberra that employs over 70 academic staff and 150 HDR students.  Over recent years SEIT has produced high impact research publications and continues to perform exceptionally in terms of research outputs. Research projects are funded through competitive funding opportunities as offered by the Australian Research Council, Industry partners and Defence, in addition to generous internal support provided by UNSW Canberra.

About the Role:

  • Job Title- Research Associate
  • Salary – Level A ($78,109- $104,143) plus super
  • Fixed Term for 18 months
  • Full Time

An exciting opportunity in the field of Electrical Engineering or Computer Science with focus on information theory, data privacy or security to contribute to an interdisciplinary project. This is a fixed term position available for 18 months. This project aims to use a range of advanced theories and tools from information theory, optimization, and computer science to tackle the problem of high-fidelity (high-utility) data sharing in large-scale and complex systems with strong privacy guarantees, to develop new theories and techniques for strong protection of personal information in sharing data. Example applications include, but are not limited to sharing health data, financial data, or census data with selected researchers or trusted agencies, as well as IoT applications such as analysing energy usage data, agricultural data, and so on.

About the project:

Most businesses collect increasingly large amounts of data about their customers or users. Such data is a critical business asset or resource for research. For example, when analysed using machine learning algorithms, it enables targeted advertising or enhances productivity. It can also be traded on various data marketplaces or shared between business partners. While user data contains useful information (e.g., purchasing habits) that unlock such added values, it can explicitly or implicitly contain users’ private information (e.g., financial situation). In addition, governments and public organisations hold very large datasets such as census records or health records, that if used responsibly, can help public and private organisations plan for a better future for their people. As data custodians, governments and businesses have both legal and ethical obligations to protect sensitive user information.

A research thrust has been designing provable mechanisms that transform such data to maintain its usefulness for a given task/analysis when shared with a third party, while minimizing the capability of such party to infer the sensitive information. Achieving a good balance between utility and privacy in shared data remains a challenging research topic and a barrier for businesses and governments in trading or sharing more datasets.

A main research trust in this project will be extending and meaningfully connecting different notions of privacy in computer science, most notably differential privacy, with notions of privacy in information theory, namely mutual information, information density, alpha-mutual-information leakage and maximal information leakage under a unified umbrella. Such unified treatment will crucially enable joint optimization of privacy and utility, providing system-level design guidelines on how to choose different system parameters to dial up and down the resulting privacy and utility performance. This project will consider complex real-world practical scenarios where data is longitudal (exhibiting time-dependence or other types of cross-correlation) or is dynamic (gradually added or changing in behaviour).

About the Successful Applicants:

 To be successful in this role you will have

  • PhD in Electrical Engineering or Computer Science in a cognate topic to this project
  • Strong mathematical and analytical skills
  • Evidence of original and high-quality research publications in top-tier journals and conferences in areas related to this project
  • Excellent written or oral communication skills
  • Evidence of independent research
  • Evidence of project teamwork

Persons with research experience in information theory, communication, data systems, privacy or security in conjunction with experience in supervision of undergraduate or postgraduate projects are welcome to apply.

In your application you should systematically address the selection criteria outlined in the Position Description. In order to view the position description - please ensure that you allow pop-ups for the Jobs@UNSW Portal.

An applicant will be required to undergo pre-employment checks prior to appointment to this role.

For further information about UNSW Canberra, please visit our website: UNSW Canberra


Dr Neda Aboutorab
School of Engineering and Information Technology
T: 02 51145110

Applications Close:  11:30pm, Sunday 25 April 2021 

Find out more about working at UNSW Canberra

            UNSW is an equal opportunity employer committed to diversity

       Any questions about the application process - please email

Advertised: 16 Mar 2021 AUS Eastern Daylight Time
Applications close: 25 Apr 2021 AUS Eastern Standard Time

In your application, please refer to