ºÚÁÏÍø´óʼÇ

We are offeringÌýtwo fully funded PhD scholarshipsÌý²¹³ÙÌýºÚÁÏÍø´óÊÂ¼Ç Sydney, open toÌýdomestic candidatesÌýwho are passionate about advancing public health through innovative research inÌýinjury surveillanceÌý²¹²Ô»åÌýdata science. Study must take place in Sydney, Australia for the majority of candidature. Each student will have the opportunity of a 6-month placement within theÌýAustralian Institute of Health and WelfareÌý(AIHW) during their candidature (Sydney or Canberra office).Ìý

Each scholarship provides:

• Tax-free stipend of $40,505 per annum (2026 rate)
  • indexed annually: $42,614 (2027), $44,845 (2028)
  • $5,000 above the current base RTP stipend rate, funded through MRFF research grant
• Support for research-related costs, including
  • conference travel
  • training and professional development
  • secure research environment access where required
• AIHW placement in relevant team, for project related work experience
• Duration:Ìý3 years full-time, with theÌýpotential for a 6-month funded extensionÌý(subject to project needs, satisfactory progress, and available funding at the time).

Note:ÌýThis is aÌýstipend-only scholarship. Domestic students are generally eligible for aÌýCommonwealth Research Training Program (RTP) Fee Offset, which covers tuition fees. Applicants should confirm their eligibility for the RTP Fee Offset with ºÚÁÏÍø´óʼÇ's Graduate Research School.

🎓ÌýAvailable PhD Projects

1. Modernising Injury Surveillance

Title:ÌýHarnessing Emergency Department Data for Population-Level Prevention
This project will explore new, scalable approaches to monitor injury patterns using routinely collected Emergency Department data. The aim is to inform public health interventions and policy responses to injury prevention, focusing on the effects of climate change, urbanisation, and healthcare system pressures. PhD by publication only. Enrolment in approved coursework subjects isrequired; subjects can be selected from theÌýÌý²¹²Ô»åÌýÌýcourses.ÌýÌý

2. AI for Injury Coding

Title:ÌýAI-Driven Classification of External Causes of Injury from Free-Text Data
This project will apply cutting-edge machine learning and large language models (LLMs) to classify and code the external causes of injury using unstructured free-text data from Emergency Department records, contributing to ICD-11 coding systems and improving injury surveillance. PhD by publication only. Enrolment in approved coursework subjects isÌýrequired; subjects can be selected from theÌýÌý²¹²Ô»åÌýÌýcourses.ÌýÌý

Eligibility & Application

• Open toÌýdomestic students onlyÌý(Australian citizens, permanent residents, or New Zealand citizens)
• Applicants must have a Master’s degreeÌýand at least two years of research experience in a relevant field:
  • For Project 1 (Injury Surveillance):ÌýMaster of Public Health (MPH) with relevant research experience
  • For Project 2 (Data Science):ÌýMaster’s degree in Health Data Science, Computer Science, or a related field
• PhD eligibility:ÌýApplicants must meet the eligibility criteria for aÌýºÚÁÏÍø´óÊÂ¼Ç PhD program. Please check the ºÚÁÏÍø´óÊÂ¼Ç Graduate Research School website for more details.
• Strong analytical, communication, and research skills are essential. Experience with large scale health data sets is also necessary.Ìý

PRIMARY SUPERVISOR:ÌýDr Lisa N. SharwoodÌý(PhD MPH GradDip Hlth Data Sci GradDip Adv Nsg BN RN)

Start Date:Ìýbetween July-Nov 2025
Location:ÌýºÚÁÏÍø´óÊÂ¼Ç Sydney
For further information, contact:Ìýl.sharwood@unsw.edu.au

BackgroundÌý

Injury surveillance data can identify injury related health risks not currently detected using standard data collections, such as domestic violence/child maltreatment, alcohol/other drug misuse, intentional self-harm, consumer product safety or workplace risk and thus inform prevention activity, implementation and evaluation. EDs collect structured and unstructured data which offers significant opportunity to better identify injury causes, intent, or location.ÌýÌýCurrently, national ED data (provided by states and territories) contain limited diagnostic codes using different classification systems and offering only codes for the injury sustained (e.g., laceration). There is vast jurisdictional variation in injury surveillance - no common method, definition or data model, some with no collection. Working with States and Territories as part of their existing data provisions to the AIHW for the ED National Minimum Data Collection (National non-admitted patient emergency department care database-NNAPEDCD), we will access their ED data (particularly free text in eMRs) then within closed environments, will locally build and implement machine learning models to develop external cause of injury codes (such as intent, location, activity, mechanism, perpetrator etc). The AIHW’s Coding Classifications experts will validate these codes against their manual processes, which will also enable use cases for the validation and potential introduction of ICD-11 into Australia. Importantly, participating jurisdictions will have their own surveillance system built, that is able to be used for their own surveillance and reporting purposes but built with common data models and outputs that can ultimately be connected to become a national surveillance system. The external cause of injury codes will be sent back up to the AIHW as part of the NNAPEDCD.

Funding and Partnership

NISAR-ED is funded by the Medical Research Future Fund ($2.98M) in the 2023ÌýÌýinitiative to will build a National Injury Surveillance system, better identifying the external causes, intent and location of injuries treated in Emergency Department (ED)s at Australian hospitals.Ìý

OurÌýkey partnersÌýinclude the Australian Institute of Health and Welfare (AIHW), the Australian Competition & Consumer Commission (ACCC), the Australasian College of Emergency Medicine (ACEM) and Monash University Accident Research Centre (MUARC).Ìý

Investigator Team

The highly experienced transdisciplinary investigator team includes representatives from these key partners, and experienced academics across numerous domains, some of whom are funded in the grant to conduct key pieces of embedded work in this project:ÌýÌý

: Project Lead, Injury Epidemiologist | ºÚÁÏÍø´óÊÂ¼Ç Senior Research Fellow

ÌýPresident of International Federation of Health Information Management Association; Head of Pacific Health Information and Classifications Support Unit, AIHW

: Director, Victorian Injury Surveillance Unit, MUARC

ÌýAM: Head, School of Population Health, ºÚÁÏÍø´óʼÇÌý

: Founding Director, Centre for Big Data Research in Health (CBDRH), ºÚÁÏÍø´óʼÇ

:ÌýÌýHead, Injuries & System Surveillance, AIHW

Dr Oscar Perez Concha:ÌýÌýSenior Lecturer in Advanced Machine Learning, CBDRH, ºÚÁÏÍø´óʼÇ

: Macquarie University, Centre for Healthcare Resilience & Implementation Science

: Professor of Law and Ethics, Deputy Head of School & Deputy Dean, USYDÌý

: Director, Macquarie University Centre for the Health Economy

: Emergency Physician, Director RPA Green Light Institute

Professor Fiona Shand: Head, Suicide Prevention Research, The Black Dog Institute, ºÚÁÏÍø´óʼÇ

: Lead, Safer Families Centre of Research Excellence. Chair in Family Violence Prevention, University of Melbourne

: NHMRC CRE in Better Health Outcomes for Compensable Injury, USYD

Key engagement from the AIHWÌýalso includes:Ìý

: Unit Head, Self-Harm and Suicide Monitoring, AIHW

: Unit Head, Family, Domestic and Sexual Violence Unit, AIHW

Experts by experienceÌýwill be provided by:Ìý

Ìýexperts in family and domestic violence: Safer Families at the University of Melbourne

Ìýadvisors in self-harm and suicidality.

Key Objectives of NISAR-ED

• NISAR-ED will develop at a jurisdictional level, the first version of a machine learning derived set of external cause of injury codes, using ED data and ED eMR free text.
• Each jurisdiction’s injury surveillance can ultimately be combined to feed into a series of national dashboards, showing up to date information on current and emerging injury risks.Ìý
• Provide accurate and timely data that can be used for surveillance – a cloud-based early detection and monitoring system that can help the public health community protect Australians from injuries, reduce injury treatment burden and evaluate interventions.

Are you an Aboriginal or Torres Strait Islander person interested in obtaining a higher degree? A generous scholarship is now available from The School of Population Health and ºÚÁÏÍø´óÊÂ¼Ç for a Masters or PhD in Aboriginal youth health and wellbeing.

We are seeking expressions of interest from candidates to undertake Masters by Research (MRes), Masters in Philosophy (MPhil) or a Doctor of Philosophy (PhD). There are a several potential research topics open including:

• Understanding how factors related to social and emotional wellbeing, cardiometabolic risk, and avoidable injury vary across the adolescent age spectrum in the ‘Next Generation’ cohort.
• Exploring how Aboriginal cultural health, health behaviours and social challenges impact young Aboriginal people’s health and wellbeing in the ‘Next Generation’ cohort
  
• Working collaboratively with Aboriginal communities and stakeholders to explore preventive actions and knowledge translation.

Why study with us?

We have generous tax-free scholarships available for Aboriginal and Torres Strait Islander higher degree students providing support up to $60,000 pa, and a strong team of Aboriginal academics to provide cultural and professional support.

You will have the opportunity to engage in high quality research under the guidance of expert researchers and supervisors. Our School is strongly committed to excellence in learning, teaching and research to enhance public health and health services. We provide quality learning that is student-centred and relevant to real-world practice.

Who we are looking for

If you are an Aboriginal or Torres Strait Islander person with an undergraduate deg