Postdoctoral Researcher in Medical Data Analysis

Melbourne, Australia
09 Mar 2023
End of advertisement period
23 Mar 2023
Contract Type
Fixed Term
Full Time
  • Postdoc researcher role within Swinburne University
  • Full time, 18-month fixed term position at our Hawthorn campus
  • Academic Level B salary + 17% superannuation

About the Role

The Swinburne University of Technology Computing Technologies department is looking for a Postdoctoral Researcher in Medical Data Analysis.

The position of Postdoctoral Researcher in Medical Image Analysis represents an exciting opportunity to play a key role in a major research collaboration between Swinburne University of Technology, Max Kelsen, and the Peter MacCallum Cancer Centre (Peter Mac) as part of a 3-year project funded by the Digital Health Cooperative Research Centre (CRC).  This role will contribute to the development of novel computer vision and deep learning methods utilising multi-modal datasets made available through a real-world patient data management platform designed for the responsible development of AI-enabled clinical decision support systems.

Working with project partners, this role will leverage the capabilities of this platform to create novel medical data analysis and diagnostic methods – including deep learning and related approaches – by utilising curated data from multiple data sources.

About You

To be suitable for this role you will need to have experience in the below key accountabilities: 

  • At least one year of prior post-doctoral research experience
  • Experience in the development of computer vision and/or deep learning models for image analysis (in particular segmentation problems)
  • Demonstrated capacity to work collaboratively and proactively as part of a multi-disciplinary team, as well as proven ability to work effectively with minimal supervision
  • Ability to contribute to research projects including identifying and tracking milestones, reporting on progress and identifying barriers to completion


  • Completion of a doctoral qualification in a relevant field (e.g. computer vision, machine learning, deep learning) with proven knowledge of research techniques and methodologies.  Prior experience with medical data analysis is beneficial, though not essential

About Swinburne University of Technology

Swinburne Horizon 2025 draws upon our understanding of future challenges. With this new strategic plan, we choose to build Swinburne as the prototype of a new and different university – one that is truly of Technology, of Innovation and of Entrepreneurship, and proud of it. We are committed to a differentiated university proposition in education and research, so that:

  • Every Swinburne learner gets a work experience
  • Every Swinburne graduate gets a job
  • Every Swinburne partner gets a tech solution
  • Swinburne is the prototype of global best practice

The achievement of our 2025 moon shots depends on our capacity to work collectively, always, as One Swinburne.

To Apply

Please submit your CV and cover letter addressing your suitability for this position.

To review the Position Description and to apply, please scroll down to the bottom of the page.

If you are viewing this advert from an external site, please click ‘apply’ and you will be redirected to Swinburne’s Jobs website to access the Position Description at the bottom of the page.

Please Note: Appointment to this position is subject to passing a Working with Children Check.

If you are experiencing technical difficulties with your application, please contact the Swinburne Talent Acquisition Team on staffrecruitment@swin.edu.au

Applications Close: Thursday 23rd March 2023, 5 pm 

Swinburne offers flexible working options, leave and parenting/carer policies to support work life balance.

Equity and Diversity 

Swinburne is a large and culturally diverse organisation and we are proud of our commitment to equity, diversity and inclusion through key initiatives.  For further information on all our initiatives visit our Equity & Diversity website.

We welcome and encourage applicants from diverse backgrounds to apply.

We are committed to making the recruitment process fair and equitable for all our candidates. If you have specific accessibility or support requirements please contact inclusion@swin.edu.au

Aboriginal and Torres Strait Islander Applicants

We welcome and strongly encourage applications from Aboriginal and Torres Strait Islander people.  

For any support please contact DeadlyCareers@swin.edu.au or for more information on our Indigenous strategies please follow the link to our RAP Reconciliation Action Plan

We are a 2023 Circle Back Initiative Employer – we commit to respond to every applicant