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significant health and disability policy reforms. The successful candidate will undertake data-driven, policy-relevant research using advanced quantitative methods, including causal econometric analysis and
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, and how these dynamics affect access to care and population health. Using large-scale longitudinal administrative data and modern causal inference methods, the research will analyse how changes in pay
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clinical and drug development precincts at Monash and has a number of major industry partnerships to facilitate the translation of our research. For more information about the BDI please visit our website
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health, employment, and wellbeing outcomes for individuals and families. The successful candidate will join a highly collaborative research team using linked employer–employee administrative data
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degree, or High Second-Class Masters degree or equivalent in the health-related field. Strong quantitative research skills. An understanding of quantitative research methodology and data analysis. Evidence
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quantitative disciplines such as data science, mathematical statistics, actuarial science or public health or psychology with strong statistical training. You can check your eligibility with the PhD readiness
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candidates holding (or eligible to obtain) a valid student visa. Candidates with strong academic and research track records are particularly encouraged to apply. For further information, please follow the link
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health economics, labour economics, economics and econometrics. We will also consider other quantitative disciplines such as data science, mathematical statistics, actuarial science or public health
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results. For full information on scholarship eligibility, please click here . For general information on applications and commencing a research degree at Monash, please click here . Apply for a scholarship
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, computational modelling, and data-driven alloy design to: Understand the mechanisms of local austenite-to-ferrite transformation in low-alloy steels; Develop frameworks to predict and control