Research Fellow- Centre for Big Data Research in Health

30 ago - Italia
UNSW

This Job is based in Australia
- Employment Type: Tempo pieno; 35 hours per week
- Duration: Fixed Term until 31 December 2028
- Remuneration: Academic Level B (133,065 - )156,864 per annum (based on experience) + 17% Super + Annual Leave Loading
- Location: Sydney, New South Wales, Australia

Position Summary
The Research Fellow will join the Centre for Big Data Research in Health to undertake innovative research focused on improving the prevention and management of adverse events in hospitalised patients through advanced data science and artificial intelligence. Reporting to the Technical Lead of CardiacAI, the role will contribute to the development, validation and evaluation of predictive algorithms, clinical decision-support tools, and data visualisations that enhance patient care and clinical decision-making.
Working collaboratively within a multidisciplinary research team, the Research Fellow will design and conduct high-quality research using large-scale healthcare datasets, applying statistical,



machine learning and natural language processing methods to address clinically relevant questions. The role will contribute to research publications, grant applications, ethics submissions and conference presentations, while supporting research students and fostering collaborative research initiatives. The Research Fellow will also promote reproducible research practices and contribute to the Centre’s ongoing research excellence. The role has no direct reports.
Some Key Skills Required
- A PhD in data science, biostatistics, computer science, health informatics or a closely related field.
- Demonstrated track record in research with outcomes of high quality and high impact with clear evidence of desire and ability to achieve research excellence as well as the capacity for research leadership.
- Strong understanding of theory and methods relating to the design of research studies using largescale observational health and medical data and methods for the analysis of t

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