Skip to main content

This job has expired

Lecturer in Computing (loT and Networking), Faculty of Science and Engineering

Employer
MACQUARIE UNIVERSITY - SYDNEY AUSTRALIA
Location
North Ryde, Australia
Salary
$108,111 to $127,913 per annum, plus 17% employer’s superannuation and annual leave loading
Closing date
31 Aug 2022
  • Excellent research and collaboration opportunities
  • Progressive flexible working arrangements
  • Designated teaching delivery and research support

We are looking for an outstanding early career academic with an excellent track record of teaching and research in Internet of Things (IoT) and Networking. The successful applicant will be expected to develop, teach and convene units at the postgraduate and undergraduate levels, while supervising Higher Degree Research students as well as leading and collaborating on research projects in IoT/Networking.

The successful candidate will demonstrate an excellent track record in research and teaching, as well as personal and professional skills that can enable service and leadership contributions that will help to strengthen the impact and reputation of the University.

About Us

You will be joining a growing School of Computing in the Faculty of Science and Engineering with a fast-rising reputation (Top 200 for Computer Science and Information Systems, in 2022 QS World University Rankings by Subject) and substantial new hiring. The School of Computing is currently the home of 42 academic staff and more than 100 research students, and an ever-growing cohort of undergraduates and postgraduate coursework students. The School offers a broad range of cutting edge undergraduate and postgraduate courses and enjoys a strong research ethos.

Salary and Benefits

  • Full-time, continuing position
  • Salary package: Level B from $108,111 to $127,913 per annum, plus 17% employer’s superannuation and annual leave loading

In addition, we offer,

  • Career development opportunities such as academic promotions and pathways, secondments, performance review and career planning
  • Designated teaching delivery and research support
  • Flexible working options and genuine work-life balance
  • Salary packaging options & corporate health discounts
  • Generous leave entitlements including paid parental leave
  • Employee Assistance program (EAP)
  • Childcare centres and sport and Aquatic Centre

How to Apply

To be considered for this position, please apply online by submitting your CV and a separate document responding to the selection criteria below:

Essential Criteria

  • A PhD in Computing or a closely related discipline
  • Demonstrated research activities in IoT/Networking closely aligned with the existing relevant areas of excellence of the School which include mobile and ubiquitous systems, IoT data analytics, embedded and edge AI, and low-power wide area networks.
  • Established research track record including publications in top journals and conferences.
  • Demonstrated track record of teaching excellence in the core areas of IoT with a strong commitment to teaching and learning at undergraduate and postgraduate levels, including experience in lecturing and curriculum development
  • Ability to form effective collaborations in research, teaching and service.
  • Proven ability to successfully attract and supervise research students
  • Demonstrated potential to attract nationally competitive and/or industry research grants
  • Demonstrated excellent written and interpersonnel skills with an ability to interact and liaise effectively with various stakeholders including students, staff, industry and the wider community

Desirable Criteria

  • Prior relevant experience in delivering (online) short training courses or micro-skills modules

Enquiries: Professor Michael Sheng, Head of School, Computing at michael.sheng@mq.edu.au

For further information and to apply, please visit www.jobs.mq.edu.au

Applications Close: 31/08/2022 11:59PM (AEST)

Get job alerts

Create a job alert and receive personalised job recommendations straight to your inbox.

Create alert