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Research Associate in AI-driven Biomedical Health Informatics

Apply now Job no: 509802
Work type: Full time
Location: Sydney, NSW
Categories: Post Doctoral Research Associate

The Opportunity

As a Research Associate you will will perform research and support this project by developing solutions for new challenging problems in biomedical health informatics. In this role you will work collaboratively with chief investigators across the Faculties of Medicine, Science and Engineering. To be successful in this role you must have experience in Machine Learning for non-imaging.

This position will give you the opportunity to develop your research skills and output across contributing to the writing of scientific papers and reports for international journals, participating in conferences and workshops, assist in the supervision of research students and actively engaging with industry partners.

The Research Associate will report to Professor Arcot Sowmya and has no direct reports.

  • Salary, Level A - $99,352 to $106,237 per annum + 17% superannuation
  • Full time
  • Fixed-term contract – 2 to 3 years
  • Location: Kensington – Sydney, Australia

About UNSW

UNSW is currently implementing a ten-year strategy to 2025 and our ambition for the next decade is nothing less than to establish UNSW as Australia’s global university. This position is based within the School of Computer Science and Engineering.

Skills & Experience

  • PhD (or soon to be awarded) in AI-driven biomedical health informatics or closely related area, with experience in Machine Learning for non-imaging based clinical data, including temporal datasets
  • Demonstrated ability to conduct independent research with limited supervision.
  • Demonstrated track record of publications and conference presentations relative to opportunity.
  • Demonstrated ability to work in a team, collaborate across disciplines and build effective relationships.
  • Demonstrated ability to identify and communicate implications of research findings to supervisor and research team.
  • Strong interpersonal skills with demonstrated ability to communicate and interact with a diverse range of stakeholders and students.
  • Ability to supervise undergraduate students or equivalent experience in the supervision of junior staff in related projects
  • Expertise in (and ability to learn others if necessary):
  • one or more of R, Stata, scikit-learn;
  • one or more deep learning tools such as TensorFlow, Keras, PyTorch;
  • An understanding of and commitment to UNSW’s aims, objectives and values in action, together with relevant policies and guidelines.
  • Ability and capacity to implement required UNSW health and safety policies and procedures.
  • Create a scholarly impact in the discipline which is recognised by peers in advancement of disciplinary knowledge.
  • Achieve a citation rate or proportion of research outputs in most prestigious outlets (e.g. A/A* or equivalent) in line with discipline and leading universities.
  • Align with and actively demonstrate the UNSW Values in Action: Our Behaviours and the UNSW Code of Conduct.
  • Cooperate with all health and safety policies and procedures of the university and take all reasonable care to ensure that your actions or omissions do not impact on the health and safety of yourself or others.

Additional details about the specific responsibilities for this position can be found in the position description.

To Apply: Please click the apply now button and submit your CV, Cover Letter and Responses to the Skills and Experience. You should systematically address the Skills and Experience listed within the position description in your application.

Please note applications will not be accepted if sent to the contact listed below.


Eugene Aves – Talent Acquisition Consultant


Applications close: 11:55 pm (Sydney time) on Sunday, 17th July 2022

UNSW aspires to be the exemplar Australian university and employer of choice for people from diverse backgrounds.

UNSW aims to ensure equality in recruitment, development, retention and promotion of staff, and that no-one is disadvantaged on the basis of their gender, cultural background, disability, sexual orientation or identity. We encourage everyone who meets the selection criteria to apply.

Position Description

Advertised: AUS Eastern Standard Time
Applications close: AUS Eastern Standard Time

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