Postdoctoral Research Fellow – ARC Training Centre for Information Resilience

• ARC Training Centre for Information Resilience (CIRES): School of Electrical Engineering & Computer Science

• Full-time (100%), fixed-term position through to 30 April 2026

• Base salary will be in the range $78,871.35 - $105,004.02 + 17% Superannuation (Academic Level A)

• Based at our St Lucia Campus

About This Opportunity 

This is an exciting opportunity for a Postdoctoral Research Fellow to contribute to innovative research developments within the scope of multimodal clinical data mining and will assist with developing a prediction system for paediatric sepsis in the paediatric intensive care unit (PICU).

This multisite position will be mainly situated at the Australian Research Council (ARC) Industrial Transformation Training Centre for Information Resilience (CIRES) at UQ and will involve collaboration across various projects with our government partner, Queensland Health. The successful candidate will also work at the Child Health Research Centre (CCHR), collaborating with healthcare professionals to ensure the clinical relevance of the models developed. As a research focused academic at level A the incumbent will be supported and guided by more senior academic research staff with the expectation of an increasing degree of autonomy over time.

This position will involve working closely with Data Science academic, A/Prof Sen Wang, from the School of Electrical Engineering & Computer Science.

Key responsibilities will include:

Research:

• Conduct innovative and reproducible research in multimodal medical data mining, including but not limited to bedside monitoring data, laboratory test results, demographic information, radiology results and other clinical reports, with a specific focus on predicting pediatric sepsis in PICU.

• Develop innovative deep learning models to effectively process and integrate multimodal time series data.

• Design and implement efficient models and data processing pipelines that can operate under resource-constrained environments, ensuring that developed solutions are feasible for deployment in clinical settings with limited hardware capabilities.

• Collaborate with healthcare professionals to ensure the clinical relevance of developed models and validate their performance using real-world clinical data from the partner hospital.

• Utilise best practice research methodologies, and participate in project discussions.

• Participate in the development of open-source tools and frameworks to support the wider research community in leveraging multimodal data for clinical predictions.

Supervision and Researcher Development:

• Supervise students, recruit and manage employees, provide performance feedback and training, resolve conflicts, and ensure correct remuneration and benefits.

Citizenship and Service:

• Develop partnerships, demonstrate leadership through mentoring, engage in internal service roles and committees, perform administrative functions, provide support to colleagues, and uphold university values.

This is a research focused position. Further information can be found by viewing UQ’s Criteria for Academic Performance.

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.

At the core of our teaching remains our students, and their experience with us sets a foundation for success far beyond graduation. UQ has made a commitment to making education opportunities available for all Queenslanders, regardless of personal, financial, or geographical barriers.

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, flexible working arrangements including hybrid on site/WFH options and flexible start/finish times, and genuine career progression opportunities via the academic promotions process.

About You 

• Completion or near completion of a PhD in Computer Science, Data Science, or a related field, with a strong focus on deep learning and artificial intelligence.

• Demonstrated expertise in developing and applying advanced deep learning techniques, preferably in handling and integrating multimodal data or time series data.

• Demonstrated expertise in developing efficient models for data processing in environments with limited computing resources.

• Strong programming skills in Python and familiarity with deep learning frameworks such as TensorFlow or PyTorch.

• Experience in conducting research projects, demonstrating high-level written and oral communication skills. 

• Peer-reviewed publications in high-impact journals or premiere conferences relevant to data mining or data science, e.g., NeurIPS/ICLR/ICML, KDD/ICDM, AAAI/IJCAI, WACV/BMVC/CVPR, and ACM/IEEE transactions. 

• Ability to work both independently and as a member of a cross-disciplinary research team. 

Desirable 

• Demonstrated experience in working on long-term (at least one year) research projects.

• Ability to develop industry liaisons and professional contacts.

• Experience in collaborating with clinical professionals and an understanding of the challenges associated with handling sensitive medical data.

• Broad knowledge of industry solutions and tools in data science.

• Experience in supervising research students (e.g., PhD, MPhil, Honours).

The successful candidate may be required to complete a number of pre-employment checks, including: right to work in Australia, criminal check, education check, etc. 

Work Rights: 

Visa sponsorship may be available for this appointment.

 
Questions? 

For more information about this opportunity, please contact Professor Shazia Sadiq shazia@eecs.uq.edu.au and Associate Professor Sen Wang sen.wang@uq.edu.au. For application queries, please contact talent@uq.edu.au stating the job reference number (below) in the subject line.
 

Want to Apply? 

All applicants must upload the following documents in order for your application to be considered:

• Resume

• Cover letter

• Responses to the ‘About You’ section

Other Information 

UQ is committed to a fair, equitable and inclusive selection process, which recognises that some applicants may face additional barriers and challenges which have impacted and/or continue to impact their career trajectory. Candidates who don’t meet all criteria are encouraged to apply and demonstrate their potential. The selection panel considers both potential and performance relative to opportunities when assessing suitability for the role.

We know one of our strengths as an institution lies in our diverse colleagues. We're dedicated to equity, diversity, and inclusion, fostering an environment that mirrors our wider community. We're committed to attracting, retaining, and promoting diverse talent. Reach out to talent@uq.edu.au for accessibility support or adjustments.

Applications close Wednesday, 20th November 2024  at 11.00pm AEST (R-43952).