Centre for Big Data Research in Health, UNSW
Multiple Scientia PhD scholarship opportunities, based in CBDRH, are currently open for expressions of interest, closing on 20 July 2018.
These prestigious scholarships come with a tax-free stipend of $40,000 per year for four years, up to $10,000 annually to build your career and support your international research collaborations, and a program of personalised coaching and mentoring.
Real-world evidence for surgery and medical devices
Register your interest here: https://www.2025.unsw.edu.au/apply/scientia-phd-scholarships/real-world-evidence-surgery-and-medical-devices
Evidence about surgical procedures and medical devices from traditional clinical research often fails to address key questions for patients, clinicians and policymakers regarding effectiveness, tolerability and value. ‘Real-world evidence’ to fill these gaps is generated through analysis of data derived from heterogeneous patients in real-life practice settings, such as insurance claims data and electronic health records. This project will generate real-world evidence for specific surgical procedures (e.g. joint replacement) and/or devices (e.g. cardiac pacemakers) by applying cutting-edge analytics to a unique whole-of-population big data platform comprising medical and pharmaceutical claims, and emergency department, hospital inpatient and mortality datasets.
Using ‘big data’ to evaluate large-scale pharmaceutical policy interventions
Worldwide, considerable attention is focused on using big data to evaluate the impact of large scale medicines policy interventions. This program builds on our long-standing research and leverages from our unique data infrastructure that has been purpose-built to undertake medicines policy research. The primary focus of the research will be to identify contemporary policy issues (including but not limited to drug scheduling changes, listing or withdrawal of PBS medicines or negative media attention about specific drugs) and then to undertake studies quantifying the impact of interventions on medicines use and health outcomes, using the strongest quasi-experimental methods.
‘Big data’ to inform child health and development policy
Register your interest here: https://www.2025.unsw.edu.au/apply/scientia-phd-scholarships/big-data-inform-child-health-and-development-policy
Health services aim to promote positive health, developmental and social outcomes for vulnerable children from early life. This includes targeted early intervention and integrated care across service types and sectors. Identifying vulnerable children and families with distinct characteristics and health and social service use patterns that are predictive of their subsequent outcomes is key to targeting limited health resources where they are likely to have the most impact. This project will apply complex statistical and machine learning techniques to longitudinal, cross-sectoral ‘big data’ to identify vulnerable children and families that may benefit from interventions that improve child outcomes.
To apply for this job please visit www.2025.unsw.edu.au.