Role overview

The Opportunity

Join the School of Electrical Engineering and Telecommunications in the Faculty of Engineering at UNSW as a Lecturer or Senior Lecturer in a Teaching and Research role. You will contribute to research and education in fundamentals, practice, and future directions of artificial intelligence and machine learning, with a particular focus on developing new learning paradigms, models, and disruptive architectures for electronic learning systems. Alongside delivering high-quality teaching across undergraduate and postgraduate Electrical Engineering programs, you will undertake research in areas including machine learning, artificial intelligence, electronic systems, complex adaptive systems, and related emerging technologies. Through this work, you will contribute to high-impact research outcomes and strengthen the School’s capability in learning systems.

With established strengths across electrical engineering, telecommunications, signal processing, communications, energy systems, robotics, and emerging technologies, UNSW offers an exceptional environment for academics seeking to advance their research profile, contribute to interdisciplinary initiatives, and deliver outstanding teaching. Working across disciplines such as mathematics, electronic systems, and complex adaptive systems, you will help shape future directions in machine learning and artificial intelligence while contributing to research collaboration, industry engagement, and the development of the next generation of engineers and researchers.

Position Details

Salary:
Lecturer (Level B) – AUD $133,065 to $156,864 per annum + 17% superannuation
Senior Lecturer (Level C) – AUD $161,619 to $185,422 per annum + 17% superannuation
Employment type: Full-time
Appointment type: Fixed term, 2-year convertible tenure track (Fixed-term appointment with the potential to convert to continuing employment, subject to a successful review.)
Location: Kensington – Sydney, Australia
Reporting line: This role reports to the Head of School and has no direct reports.

About You

You are an academic with expertise in artificial intelligence, machine learning, learning systems, or a closely related area, with the ability to contribute to high-quality research and teaching. You are committed to advancing your research profile while delivering engaging learning experiences in the fundamentals, practice, and future directions of artificial intelligence and machine learning for undergraduate and postgraduate students.

Success in this role requires a collaborative and adaptable approach, with the ability to work across disciplines, build relationships with industry and professional networks, and contribute to the continued development of the School’s research capability in learning systems. At Senior Lecturer level, you will also bring evidence of research leadership, a nationally recognised research track record, success in attracting competitive funding, and experience mentoring colleagues and supervising higher degree research students to completion.

About UNSW

UNSW is a world‑leading institution recognised for its scale, prestige, and impact. With strong industry engagement and partnerships across sectors, UNSW provides a unique environment where academic expertise translates into real‑world outcomes. The university is home to cutting‑edge research that drives innovation and societal progress, while its excellence in teaching ensures students are prepared to lead in their fields. For academics, UNSW offers an outstanding platform to flourish — combining world‑class facilities, collaborative networks, and a culture of innovation that supports both career growth and meaningful contributions to the wider community.

The UNSW School of Electrical Engineering and Telecommunications is one of Australia's largest and most prestigious schools of its kind. Home to more than 1,500 students and over 40 academic staff, the School is internationally recognised for excellence in teaching, research, and industry engagement across electrical engineering, telecommunications, robotics, communications, and emerging technologies. For further information about our School, please visit - https://www.unsw.edu.au/engineering/our-schools/electrical-engineering-telecommunications

Skills and Experience Level B

A PhD in Electrical/Electronics Engineering or a related discipline, along with relevant work experience.
Proven commitment to proactively keeping up to date with discipline knowledge and developments.
Demonstrated experience and evidence in teaching and learning design, development and/or delivery at undergraduate and/or postgraduate levels in the fundamentals, practice, and future directions of artificial intelligence and machine learning.
Experience using and/or designing with educational technologies and online delivery methods.
Evidence of teaching effectiveness and passion for educational excellence (e.g. relevant discipline-based curriculum design and development at a variety of levels and scales).
Demonstrated knowledge of industry or experience of work-integrated learning (desirable).
Evidence of ability to support and inspire students from diverse backgrounds and support student equity, diversity and inclusion initiatives.
Demonstrated track record in research with outcomes of high quality and high impact with clear evidence of the desire and ability to continually achieve research excellence as well as the capacity for research leadership.
A track record of significant involvement with the profession and/or industry.
High level communication skills and ability to network effectively and interact with a diverse range of students and staff.
Demonstrated ability to work in a team, related to the future of machine learning such as mathematics, electronic systems, complex adaptive systems, etc.; and build effective relationships.
Evidence of highly developed interpersonal and organisational skills.
An understanding of and commitment to UNSW’s aims, objectives and values in action, together with relevant policies and guidelines.
Knowledge of health and safety responsibilities and commitment to attending relevant health and safety training.

Level C

A PhD in Electrical/Electronics Engineering or a related discipline, along with relevant work experience.
Proven commitment to proactively keeping up to date with discipline knowledge and developments.
Demonstrated experience and evidence in teaching and learning design using a range of pedagogical approaches, development and delivery of courses and programs, at undergraduate and/or postgraduate level in the fundamentals, practice, and future directions of artificial intelligence and machine learning.
Experience of implementing educational technologies and online delivery methods.
Evidence of teaching effectiveness and passion for educational excellence and fostering this in others.
Demonstrated success in initiating curriculum development and improvement.
Demonstrated experience with industry or work-integrated learning.
Evidence of ability to support and inspire students from diverse backgrounds and support student equity diversity and inclusion initiatives.
Demonstrated track record in research with outcomes of high quality and high impact with clear evidence of the desire and ability to continually achieve research excellence as well as the capacity for research leadership.
Experience in successfully recruiting and supervising high calibre students.
Demonstrated ability to work in a team, related to the future of machine learning such as mathematics, electronic systems, complex adaptive systems, etc.; and build effective relationships.
Evidence of highly developed interpersonal and organisational skills.
Demonstrated ability to interact with the profession and industry and attract funding for learning and teaching initiatives.
An understanding of and commitment to UNSW’s aims, objectives and values in action, together with relevant policies and guidelines.
Knowledge of health and safety responsibilities and commitment to attending relevant health and safety training.

Additional details about the specific responsibilities for this position can be found in the position description. This is available via JOBS@UNSW.

To Apply:

If you are interested in an academic role that focuses on conducting independent research and delivering excellent teaching, please click the “Apply Now” button and submit your CV, cover letter and responses to the Skills and Experience.

If you have had any interruption in your career, you are encouraged to include a ROPE statement as part of your application. For information on ROPE please check: https://www.arc.gov.au/about-arc/program-policies/research-opportunity-and-performance-evidence-rope-statement

Please note applications will not be accepted if sent to the contact listed below.

Contact:

For role-specific inquiries, please contact Prof Jinhong Yuan (Head of School)

E: j.yuan@unsw.edu.au

For questions regarding the recruitment process, please contact Eugene Aves (Talent Acquisition Consultant)

E: eugene.aves@unsw.edu.au

Applications close:11:50 pm (Sydney time) on Friday, 28 August 2026

As part of our recruitment process candidates may be required to undergo pre-employment screening, which may include reference checks, qualification verification, right-to-work verification, and criminal history screening where relevant to the role.

UNSW is committed to evolving a culture that embraces equity and supports a diverse and inclusive community where everyone can participate fairly, in a safe and respectful environment. We welcome candidates from all backgrounds and encourage applications from people of diverse gender, sexual orientation, cultural and linguistic backgrounds, Aboriginal and Torres Strait Islander background, people with disability and those with caring and family responsibilities. UNSW provides workplace adjustments for people with disability, and access to flexible work options, including working from home, for eligible staff. UNSW provides 26 weeks of fully paid maternity or primary carers leave upon commencement of employment (subject to eligibility). UNSW is committed to enhancing the experience of ECAs by providing a supportive and collegial work environment and a dedicated framework to deliver support services to assist their research, teaching, and career development. The University reserves the right not to proceed with any appointment.