Research Fellow – Epidemiology Full-time NEW

Research Fellow

Join a globally recognized institution where your research will drive real-world impact. At UQ's School of Public Health, we're seeking a passionate Research Fellow to advance statistical methodologies and contribute to a new data product that integrates model outputs into our platform – helping us better understand environmental exposures and health outcomes.

This is a research-focused academic position that will see you:

• Lead and contribute to high-impact research in environmental epidemiology and statistical modeling.
• Develop and publish innovative methodologies for association analysis.
• Collaborate across disciplines to secure competitive research funding and translate findings to end users.
• Supervise and mentor HDR students, fostering the next generation of public health researchers.
• Engage with industry, government, and community stakeholders to build meaningful partnerships.

Ranked 30th globally for Public Health, UQ's School of Public Health is a leader in impactful research and world-class teaching across biostatistics, epidemiology, and public health. With strong ties to the World Health Organization and other global bodies, our work shapes policy and improves lives.

As part of the UQ community, you'll be welcomed into a vibrant, inclusive, and collaborative environment. You'll have access to world-class facilities, flexible work arrangements, and a culture that values your contribution and supports your growth.

At UQ, we're creating change together – through research, teaching, and professional excellence.

About You:

• A PhD in epidemiology, biostatistics, health data, environmental health, or statistical modeling.
• A growing research profile in environmental epidemiology or quantitative public health.
• Expertise in statistical modeling and programming (e.g., SAS, R, Python, STATA).
• Experience managing and analyzing large, complex datasets.
• Strong communication skills to translate complex methodologies for diverse audiences.
• A collaborative mindset and ability to engage with multidisciplinary teams and external partners.

Desirable attributes include experience with spatio-temporal models, machine learning, Bayesian methods, and knowledge of environmental exposure metrics.

You must have unrestricted work rights in Australia for the duration of this appointment. Visa sponsorship is not available.

Contact Associate Professor Nicholas Osborne at n.osborne@uq.edu.au for role-related queries. For application queries, reach out to talent@uq.edu.au and quote the job reference number R-54884.

Applications close Monday 13 October 2025 at 11.00pm AEST (R-54884). Please note that interviews have been tentatively scheduled for Wednesday 22nd October 2025.