About the opportunity

The Sydney School of Health Sciences is a world leader in health science research and education. We collaborate with external partners and engage with real-world problems to anticipate the health issues of tomorrow while overcoming the challenges of today. Our high-impact research is acknowledged and translated into practice and policy nationally and internationally. Our multidisciplinary research themes and centres reflect the strength, diversity and depth of our research. We engage in key issues, such as healthcare reform and strive to achieve the best possible health and wellbeing outcomes for the whole community.

The university is seeking to employ a Postdoctoral Research Associate (Level A step 7) to contribute to a breast cancer research program focused on developing, validating, and translating artificial intelligence tools for medical imaging. The role will support the design of deep learning and predictive models for tumour segmentation, cancer detection, risk prediction, and outcome modelling using multi-modal imaging and linked clinical data. The appointee will work across technical development, data curation, methodological evaluation, and integration of AI-driven solutions into clinically relevant workflows.

Your key responsibilities will be to:

Conduct high-quality research, scholarly activity, and technical development independently and as part of a multidisciplinary breast cancer AI research team
Develop and refine independent research capability under the guidance of senior academic staff while contributing specialist expertise to project design and delivery
Develop deep learning models for medical image analysis tasks including segmentation, classification, lesion characterisation, and representation learning
Develop predictive models and risk prediction frameworks using imaging, clinical, and longitudinal follow-up data, including statistical modelling and survival analysis.
Process, curate, quality-check, and analyse DICOM imaging datasets across modalities including DBT, mammography, CEM, ultrasound, CT, and X-ray
Apply highly specialised technical skills in medical image processing, deep learning, statistical modelling, and data harmonisation for multi-modal research datasets
Support the development, validation, and benchmarking of AI models for breast cancer detection, tumour segmentation, and patient-level risk prediction
Contribute to the translation of AI-driven solutions into clinical and research workflows, including documentation, reproducibility, and stakeholder engagement.
Provide informal mentoring and technical guidance to students, research assistants, and junior team members within the discipline
Assist with preparation of ethics materials, data governance documentation, manuscripts, reports, conference submissions, and grant-related outputs.
Carry out administrative tasks and contribute to project, faculty, and school meetings as required
Contribute to a positive workplace culture in which collaboration, diversity of thought, technical excellence, and responsible innovation are valued and enabled.

About you

PhD in medical imaging or a related field
Experience in breast imaging research
Experience in developing risk prediction frameworks using imaging, clinical, and longitudinal follow-up data, including statistical modelling and survival analysis
Demonstrated experience in developing deep learning models for medical imaging applications, including segmentation models and weakly supervised lesion analysis pipelines
Strong experience with DICOM data handling and image processing across DBT, mammography, and contrast-enhanced mammography
Strong experience in observer studies, including human–AI interaction studies, evaluation of AI impact on clinical decision-making, and human vs AI comparison, as well as reader performance evaluation, inter-reader agreement analysis, kappa statistics, and related assessment methods
Familiarity with model validation across multiple sites, scanners, or imaging vendors
Proficiency in Python and relevant research toolchains for machine learning, image processing, and reproducible analysis
Experience with data governance, ethics, secure health data environments, or clinically linked datasets
Experience in generative AI for medical imaging
Experience working with professional bodies, including RANZCR
Experience with privacy-preserving techniques for medical imaging
Track record of research output, such as peer-reviewed publications, conference presentations, or equivalent scholarly contributions
Ability to work effectively in a multidisciplinary team spanning technical, clinical, and academic stakeholders.

Sponsorship / work rights for Australia

You must have unrestricted work rights in Australia for the duration of this employment to be eligible to apply. Visa sponsorship is not available for this appointment.

Pre-employment checks and declarations

Your employment is conditional upon the successful completion of all pre-employment or background checks required for the role in terms satisfactory to the University. Also, to meet the University's obligations under the National Higher Education Code to Prevent and Eliminate Gender-Based Violence you will be asked to declare if you have been investigated for, or found to engaged in, sexual harm or gender-based violence in the course of previous employment or in a legal process. Similarly, your ongoing employment is conditional upon the satisfactory maintenance of all relevant clearances and background check requirements. If you do not meet these conditions, the University may take any necessary step, including the termination of your employment.

EEO statement

At the University of Sydney, our shared values are trust, accountability and excellence and we strive to be a place where everyone can thrive. We are committed to creating a University community that thrives through diversity and reflects the wider community that we serve. We deliver on this through our commitment to diversity and inclusion, evidenced by our people and culture programs, as well as key strategies to increase participation and support the careers of Aboriginal and Torres Strait Islander People, women, people living with a disability, people from culturally and linguistically diverse backgrounds, and those who identify as LGBTQIA+. We welcome applications from candidates from all backgrounds. We are proud to be recognised as an Australian Workplace Equality Index (AWEI) Platinum Employer. Find out more about our work on diversity and inclusion.

How to apply

Applications (including a cover letter, CV, and any additional supporting documentation) can be submitted via the Apply button at the top of the page. For employees of the University or contingent workers, please login into your Workday account and navigate to the Career icon on your Dashboard. Click on USYD Find Jobs and apply.

For a confidential discussion about the role, or if you require reasonable adjustment or any documents in alternate formats, please contact Rachel Yazigi, Recruitment Operations by email to r***@***********.

The University reserves the right not to proceed with any appointment.

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