Job Highlights
Amplify your impact at a world top 50 University
Be surrounded by extraordinary ideas - and the people who discover them
Shape the future of critical-care research through data innovation.
Monash University's School of Public Health and Preventive Medicine (SPHPM) is at the forefront of improving health and wellbeing for communities in Australia and across the world.
As one of the largest and most respected schools of public health in the Asia-Pacific region, SPHPM leads discovery and translation across epidemiology, biostatistics, clinical trials, health economics, genomics, and more — delivering evidence that drives equitable, real-world impact.
Within the School, the Australian and New Zealand Intensive Care Research Centre (ANZIC-RC) is a world-class research methods centre dedicated to advancing intensive-care medicine.
The Centre leads landmark studies in areas such as sepsis, traumatic brain injury, mechanical ventilation, sedation, recovery, and extracorporeal membrane oxygenation (ECMO), transforming outcomes for critically ill patients.
The Opportunity
This is a rare and exciting opportunity to join one of the world's leading critical-care research centres as a Research Fellow (Data Scientist).
Working alongside Professor Carol Hodgson and her multidisciplinary teams, you will play a key role in shaping how complex health data is captured, analysed, and translated into life-saving knowledge.
You'll contribute to major programs in ECMO and Recovery research, supporting projects that identify gaps in evidence-based care, advance post-ICU recovery science, and improve long-term patient outcomes.
Your expertise in analytics, artificial intelligence, and data management will directly inform how critical-care data is used to refine clinical practice and improve survival worldwide.
Develop and implement advanced data pipelines and predictive models using statistical, Bayesian, and deep-learning approaches.
Lead improvements in data quality, integration, and reproducibility across multi-centre trials and registries.
Collaborate with leading clinicians, engineers, and biostatisticians across Australia and New Zealand.
Contribute to high-impact publications and present findings on the global stage.
Be part of a collaborative culture that values curiosity, innovation, and purpose.
About You
You are a motivated and technically skilled data scientist who thrives on solving complex problems in health and medicine.
With strong foundations in data analytics, modelling, and statistical computing, you are ready to apply your expertise to real-world clinical challenges.
Qualifications
Postgraduate qualifications in computer science, data science, or a related discipline (PhD preferred).
Advanced skills in statistical analysis and modelling using tools such as R, Excel, Redcap, and related software.
Demonstrated experience in AI or machine-learning applications and a passion for methodological innovation.
Proven ability to manage, analyse, and interpret large or high-frequency datasets.
Excellent communication and organisational skills, with the ability to collaborate across disciplines.
Experience with clinical or health data will be highly regarded.
About Monash University
Monash is a world-leading university with five campuses in Australia, a presence in Malaysia and Indonesia, and major research centres in India, China, and Italy.
With a commitment to academic freedom and world-class research facilities, Monash offers an inclusive and supportive environment for researchers.
Monash supports flexible and hybrid working arrangements.
We have policies in place enabling staff to combine work and personal commitments, including support for parents.
Diversity is one of our greatest strengths at Monash.
We encourage applications from Aboriginal and Torres Strait Islander people, culturally and linguistically diverse people, people with disabilities, neurodivergent people, and people of all genders, sexualities, and age groups.
We are committed to fostering an inclusive and accessible recruitment process.
If you need any reasonable adjustments, please contact us at (email protected) with the subject line 'Reasonable Adjustments Request'.
Your employment is contingent upon the satisfactory completion of all pre-employment and/or background checks required for the role, as determined by the University.
Applications Close: Tuesday, 18 November at 11:55pm AEDT
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