The School of Electrical Engineering and Computer Science is seeking a visionary and collaborative academic to strengthen its systems capability at the intersection of database systems, data engineering and artificial intelligence. As a Lecturer or Senior Lecturer in Data Systems and Engineering, you will help establish data systems engineering as a strategic research strength within the Data Science discipline. Working in an inclusive, interdisciplinary environment, you will advance end-to-end data engineering for modern Data × AI workloads and translate analytical and AI capability into robust, scalable and externally deployable infrastructure.

Key responsibilities will include:

Research:

Develop and sustain an internationally recognised research program in data engineering, data systems, and mission-driven Data × AI, producing high-quality publications, competitive research funding, deployable systems, and impactful interdisciplinary research outcomes.

Teaching:

Deliver high-quality teaching across undergraduate and postgraduate programs through course coordination, innovative curriculum design, student supervision, assessment, and the development of contemporary courses aligned with emerging areas such as data engineering, AI-ready data infrastructure, and data agent systems.

Supervision and Researcher Development:

Supervise Honours, Masters and Higher Degree by Research students, support their research training and career development, and grow data systems engineering capability through mentoring and collaborative project development.

Citizenship and Service:

Develop purposeful partnerships with industry, government, universities, professional bodies and the broader community, contribute to academic leadership and collegial service, and uphold UQ's values in everything you do.

In addition to the responsibilities above, Academic Level C will be expected to:

Demonstrate academic leadership through sustained success in supervising Higher Degree by Research students, leading collaborative research programs, and mentoring early career researchers.
Secure and manage competitive external research funding as a Chief Investigator, while leading high-impact research outputs and strategic national and international collaborations.
Lead the development and enhancement of academic programs, including course coordination, curriculum innovation, and the delivery of contemporary, industry-relevant education.
Strengthen the School's profile through leadership, industry engagement, partnerships, and the translation and commercialisation of research outcomes.

This is a teaching & research position. Further information can be found by viewing UQ's Criteria for Academic Performance.

About You

Essential

A PhD in Computer Science, Data Science, Software Engineering, or a closely related discipline.
Demonstrated research expertise in data engineering, database systems, AI for data systems, or large-scale data infrastructure, with an emerging national or international research profile appropriate to career stage.
Expertise in one or more areas such as scalable query processing, database internals, indexing and storage systems, semantic query processing, AI-enhanced data processing, multimodal data management, or deployable data infrastructure.
A strong publication record in leading peer-reviewed venues relevant to data systems and databases (e.g. SIGMOD, VLDB, CIDR), with relevant systems conferences (e.g. OSDI, NSDI, SOSP) also recognised.
Demonstrated ability to design, implement, and evaluate complex data-intensive systems, with a track record of translating research into deployable prototypes, open-source software, or externally adopted technical artefacts that deliver real-world impact.
Demonstrated ability or strong potential to secure competitive research funding, contribute to collaborative and interdisciplinary research, and build partnerships with industry or other external stakeholders.
Demonstrated ability or strong potential to deliver high-quality teaching and supervision at undergraduate and postgraduate levels, including curriculum development in data engineering, data systems, or related areas.
Experience in database platforms, systems software, or data infrastructure development using languages such as C/C++, Java, Rust, Go, or Python.
Excellent communication, interpersonal, organisational, and collaboration skills, with the ability to contribute effectively within multidisciplinary teams and support the growth of data engineering capability through teaching, supervision, and research leadership.

Strategic Fit

This position supports the School's strategic vision to strengthen its end-to-end Data × AI capability by establishing internationally recognised expertise in data engineering and data systems. The successful candidate will complement existing strengths in artificial intelligence, analytics, information retrieval, and domain-focused data science by developing scalable, AI-ready data infrastructure and deployable systems that enable impactful interdisciplinary research and real-world industry engagement.

In addition to the essential criteria above, candidates at Academic Level C should demonstrate:

An established national or international research reputation, evidenced by sustained high-quality publications, successful competitive research funding, and leadership of collaborative research programs.
A strong record of principal supervision of Higher Degree by Research students and demonstrated academic leadership in researcher development.
Evidence of leading curriculum development, course coordination, and teaching innovation.
Demonstrated success in developing strategic industry and research partnerships, translating research into impact, and contributing to the leadership and service activities of an academic unit.

About UQ

As part of the UQ community, you will have the opportunity to work alongside the brightest minds, who have joined us from all over the world, and within an environment where interdisciplinary collaborations are encouraged. As part of our commitment to excellence in research and professional practice in academic contexts, we are proud to provide our staff with access to world-class facilities and equipment, grant writing support, greater research funding opportunities, and other forms of staff support and development.

The greater benefits of joining the UQ community are broad: from being part of a Group of Eight university, to recognition of prior service with other Australian universities, up to 26 weeks of paid parental leave, 17.5% annual leave loading, access to exclusive internal-only vacancies, and genuine career progression opportunities via the academic promotions process.

Interested?

For more information about this opportunity, please contact Professor Helen Huang at h***@***********. For application inquiries, please reach out to the Talent Acquisition team at t***@***********, stating the job reference number (below) in the subject line. When you apply, please ensure you upload a resume, cover letter, and responses to the 'About You' section. Please note that applications received via email will not be accepted.

Other Information

Pre-employment checks may include: verification of the right to work in Australia and qualification checks. This may also include checks relating to gender-based violence matters or other integrity and conduct requirements.

Relocating from interstate or overseas? We may support you with obtaining employer-sponsored work rights and a relocation support package, however upfront costs will still be expected to be covered. You can find out more about life in Australia's Sunshine State here.

We're dedicated to equity, diversity, and inclusion. We recognise that career pathways and opportunities differ, and encourage applications from candidates who may not meet every criteria but can demonstrate their potential relative to opportunity. We're also happy to support any accessibility needs throughout the recruitment process. Just let us know how we can help by emailing t***@*********** or calling +6* * **** ****.

Applications close Sunday 6 September 2026 at 11.00pm AEST (R-67067).

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Lecturer/Senior Lecturer in Data Systems and Engineering