National Injury Surveillance for Actionable Research – Emergency Department (NISAR-ED) PhD Scholarships

We are offering two fully funded PhD scholarships at UNSW Sydney, open to domestic candidates who are passionate about advancing public health through innovative research in injury surveillance and data 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 the Australian Institute of Health and Welfare (AIHW) during their candidature (Sydney or Canberra office).

Each scholarship provides:

1. 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

2. Support for research-related costs, including

• conference travel
• training and professional development
• secure research environment access where required

3. AIHW placement in relevant team, for project related work experience

Duration: 3 years full-time, with the potential for a 6-month funded extension (subject to project needs, satisfactory progress, and available funding at the time).

Note: This is a stipend-only scholarship. Domestic students are generally eligible for a Commonwealth Research Training Program (RTP) Fee Offset, which covers tuition fees. Applicants should confirm their eligibility for the RTP Fee Offset with UNSW's Graduate Research School.

Available PhD Projects

1. Modernising Injury Surveillance

Title: Harnessing 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 is required; subjects can be selected from the PHCM and HDAT courses.  

2. AI for Injury Coding

Title: AI-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 is required; subjects can be selected from the PHCM and HDAT courses.  

Eligibility & Application

• Open to domestic students only (Australian citizens, permanent residents, or New Zealand citizens)
• Applicants must have a Master’s degree and at least two years of research experience in a relevant field:
•  For Project 1 (Injury Surveillance): Master of Public Health (MPH) with relevant research experience
• For Project 2 (Data Science): Master’s degree in Health Data Science, Computer Science, or a related field
• PhD eligibility: Applicants must meet the eligibility criteria for a UNSW PhD program. Please check the UNSW 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. 

How to apply:

Email Dr Lisa Sharwood at l.sharwood@unsw.edu.au by 30 June 2025 with the following:

• copy of your academic transcripts
• CV outlining your previous research experience, in particular the management, curation and analysis of large datasets, using open-source tools.

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

Start Date: July/Aug 2025 or by negotiation
Location: UNSW Sydney
For further information, contact: l.sharwood@unsw.edu.au

BACKGROUND:

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.  Currently, 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 2023 National Critical Research Infrastructure initiative 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.

Our key partners include 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). 

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: 

• Dr Lisa Sharwood: Project Lead, Injury Epidemiologist | UNSW Senior Research Fellow
• Ms Vicki Bennett: President of International Federation of Health Information Management Association; Head of Pacific Health Information and Classifications Support Unit, AIHW
• Associate Professor Janneke Berecki-Gisolf: Director, Victorian Injury Surveillance Unit, MUARC
• Professor Rebecca Ivers AM: Head, School of Population Health, UNSW
• Professor Louisa Jorm : Founding Director, Centre for Big Data Research in Health (CBDRH), UNSW
• Ms Jacqueline Rek:  Head, Injuries & System Surveillance, AIHW
• Dr Oscar Perez Concha:  Senior Lecturer in Advanced Machine Learning, CBDRH, UNSW
• Professor Robyn Clay-Williams: Macquarie University, Centre for Healthcare Resilience & Implementation Science
• Professor Rita Shackel: Professor of Law and Ethics, Deputy Head of School & Deputy Dean, USYD
• Professor Henry Cutler: Director, Macquarie University Centre for the Health Economy
• Professor Michael Dinh: Emergency Physician, Director RPA Green Light Institute
• Professor Fiona Shand: Head, Suicide Prevention Research, The Black Dog Institute, UNSW
• Professor Kelsey Hegarty: Lead, Safer Families Centre of Research Excellence. Chair in Family Violence Prevention, University of Melbourne
• Professor Trudy Rebbeck: NHMRC CRE in Better Health Outcomes for Compensable Injury, USYD

Key engagement from the AIHW also includes:

• Mr Chris Killick-Moran: Unit Head, Self-Harm and Suicide Monitoring, AIHW
• Ms Sally Mills: Unit Head, Family, Domestic and Sexual Violence Unit, AIHW

Experts by experience will be provided by:

• WEAVERs experts in family and domestic violence: Safer Families at the University of Melbourne
• Black Dog Institute’s Lived Experience Expert advisors 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.
• 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.