Research Fellow, Cybersecurity

Swinburne University of Technology

Vacancy closed!

Unfortunately this vacancy is no longer open. Please contact a member of staff if you require assistance.

Reference #
1385_11/20_RTR
Closing Date
14-06-2021
Salary
Full time, 3-year fixed term position at our Hawthorn campus

About the Job
Cybersecurity is integral to Australia's national security, innovation, and prosperity. As a key element of Swinburne’s Research Strategy, Swinburne is fast developing its research and innovation capabilities in Cybersecurity. Swinburne’s Cybersecurity Lab is engaged in researching and developing technologies to protect our current and future information systems and networks. These range from technologies that secure an individual’s information to those that safeguard critical infrastructure.

The Research Fellow will contribute to the research in the broad area of data-driven cybersecurity, system security, and adversarial machine learning. The purpose of this position is to research into the area of theory development and system implementation of a unified adversarial classification framework that considers uncertainty from data. The resilience of systems and models is therefore a critical component for trustworthy systems in national security and society more broadly, but one that is so far not well investigated. The successful candidate will demonstrate extensive knowledge in network and system security, machine learning algorithms, and data analytics.

About Swinburne University of Technology
Swinburne is a multi-sector university of science, technology and innovation with more than 54,000 students and 5,000 staff globally. We offer postgraduate, undergraduate, vocational education and online education to provide students with a variety of work-relevant pathways. Our mission is to be a world-class university, creating economic and social impact through science, technology and innovation. We aim to deliver future-ready learners, research with impact and innovative enterprise.

About you
To be successful in the role, you will have:
  • Relevant PhD degree in computer science and/or IT discipline
  • Extensive knowledge in machine learning and cybersecurity
  • Rich experience in machine learning modelling and implementation of algorithms

A full list of selection criteria is available within the position description.

Benefits
  • Participate in regular staff and management development programs
  • Onsite childcare
  • Private health insurance discounts
  • Discounted annual Myki cards are available to Swinburne staff

To find out more about the extensive range of benefits offered to Swinburne employees please visit Careers at Swinburne.

Swinburne is a large and culturally diverse organisation. We are proud of our commitment to equity and inclusion through key initiatives such as our Charter of Cultural Diversity, Pride@Swinburne Strategic Action Plan, Science in Australia Gender Equity (SAGE) Action Plan and our Reconciliation Action Plan. Equity and diversity are integral to our 2025 vision to be a world class university creating social and economic impact through science, technology and innovation.

How to apply
To view the position description or to start an application please click on apply or ‘begin’ and submit a resume, cover letter and a response to the Key Selection Criteria, as listed in the position description.

For further information about this position, contact Professor Yang Xiang, Dean, Digital Research Capability Platform via email at yxiang@swin.edu.au

If you are experiencing technical difficulties with your application, please contact the Recruitment team on staffrecruitment@swin.edu.au

Should you require further support for an interview due to special needs or consideration, please contact Maree Norden, Diversity & Inclusion Manager at inclusion@swin.edu.au. For support or queries related to Aboriginal and Torres Strait Islander employment, please contact DeadlyCareers@swin.edu.au

Agency enquiries will not be accepted for this position.

Applications close at 5pm on Monday 14th June 2021.

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