Postdoctoral Research Fellow/Rf In Synthetic Clinical Data

• School of Electrical Engineering and Computer Science
• Join a university ranked in the world's top 50
• An exciting chance to conduct research in the Generative AI space alongside a collaborative team.
• Experience with medical data and a background in computer science or applied statistics will be highly regarded.
• Based at our vibrant and picturesque St Lucia Campus

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.

This is an exciting opportunity for a Postdoctoral Research Fellow/Research Fellow to focus their efforts on developing their expertise and emerging research profile in Synthetic Clinical Data generation and federated learning.

Access to Clinical Data for medical research and education is often difficult because of lengthy data sharing agreements and privacy concerns. Two complementary approaches to facilitate access to Clinical Data are:

• the generation and distribution of Synthetic Clinical Data, i.e. data with similar statistical properties as the real data that was used to generate them, but without identifiable patient information.
• federated learning, whereby a machine learning model can be trained by using data from different clinical sites but without requiring central data aggregation.

The data used in this project will mainly consist of electronic medical records (EMRs) from hospitals but could include other modalities such as free text (e.g. clinical notes, discharge summaries) or medical images.

Key responsibilities will include:

• Research
• Supervision and Research Development
• Citizenship and Service

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

This is a full-time fixed-term position for up to 3 years at Academic level A or B.

The full-time equivalent base salary at Level A will be in the range $77,324.85 - $102,945.12, plus a generous super allowance of up to 17%. The total FTE package will be up to $90,470.08 - $120,445.79 annually.

The full-time equivalent base salary at Level B will be in the range $108,201.26 - $128,201.75, plus a generous super allowance of up to 17%. The total FTE package will be up to $126,595.47 - $149,996.05 annually.

As these roles are covered by an Enterprise Agreement, you will also receive regular remuneration increases in line with the Enterprise Agreement.

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.

• Completion or near completion of a PhD in machine learning with applications in health and medicine.
• An emerging profile in machine learning research. This could include experience with generative models (e.g. variational autoencoders, generative adversarial networks, denoising diffusion probabilistic models, generative pre-trained transformers, structured state space models), vertical and horizontal federated learning, natural language processing (e.g. transformers, large language models), medical image processing, fairness in machine learning.
• Evidence of publications in reputed refereed journals and presenting at conferences.
• Understanding of the Australian research funding landscape (level A) or evidence of contributions towards successfully obtaining external research funding (level B).
• Some experience in meaningful internal service roles and contributions towards external activities

In addition, the following mandatory requirements apply:

• Work Rights: Visa sponsorship may be available for this appointment.
• Background Checks: All final applicants for this position may be asked to consent to a criminal record check. Please note that people with criminal records are not automatically barred from applying for this position. Each application will be considered on its merits.

Relocating from interstate or overseas? You can find out more about life in Australia's Sunshine State here.

For more information about this opportunity, please contact Associate Professor Sebastiano Barbieri at **********@uq.edu.au

For application queries, please contact ******@uq.edu.au stating the job reference number (below) in the subject line.

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

• Resume
• Cover letter addressing the 'About You' section

At UQ we know that our greatest strengths come from our diverse mix of colleagues, this is reflected in our ongoing commitment to creating an environment focused on equity, diversity and inclusion. We ensure that we are always attracting, retaining and promoting colleagues who are representative of the diversity in the broader community, whether that be gender identity, LGBTQIA+, cultural and/or linguistic, Aboriginal and/or Torres Strait Islander peoples, or people with a disability. Accessibility requirements and/or adjustments can be directed to ******@uq.edu.au.

If you are a current employee (including casual staff and HDR scholars) or hold an unpaid/affiliate appointment, please login to your staff Workday account and visit the internal careers board to apply for this opportunity. Please do NOT apply via the external job board.

Applications close Thursday 6th June 2024 at 11.00pm AEST (R-38697)