Research Fellow

The Research Fellow (Cancer Data Analytics & Health Services Research) will join the All-Island E-Health Hub for Cancer at Queen’s University Belfast, working in partnership with the Northern Ireland Cancer Registry. Reporting to Professor Mark Lawler, with co-supervision from Dr Ethna McFerran and Dr Damien Bennett, the postholder will undertake applied research using routinely collected cancer data to address priority questions in cancer care pathways, diagnostic delay, and treatment access. The role will involve advanced quantitative analyses, such as survival modelling, machine learning, and clustering—across large registry datasets, generating outputs that inform cancer policy and health service planning.

The Fellow will lead specific projects, contribute to north–south data harmonisation initiatives, and support development of infrastructure for the e-Health Hub. Responsibilities include publication of high-impact research, preparation of policy briefs and dashboards, engagement with stakeholders across academia, clinical services, and government, and mentoring of junior researchers.

About the person:

We are seeking a highly motivated postdoctoral researcher with expertise in health data analytics, epidemiology, or medical statistics to join the All-Island E-Health Hub for Cancer at Queen’s University Belfast, in partnership with the Northern Ireland Cancer Registry.

The successful candidate must have, and your application should clearly demonstrate that you meet the following essential criteria:

• Hold or be about to obtain a PhD (awarded / submitted*) in health data science, epidemiology, medical statistics, or a closely related area such as computer Science, or mathematics. * If PhD pending, it must be conferred ≤3 from date of interview.
• Significant, relevant research experience
• Proven track-record conducting research using large-scale routine or registry data, preferably in cancer
• High-level proficiency and experience in R, Stata, or Python, with demonstrable experience applying open-source packages for large-scale health data processing.
• Experience of UNIX/Linux, GitHub and reproducible workflow frameworks.
• Experience of use of open-source packages for data processing.
• Experience contributing to peer-reviewed publications or policy reports.

To be successful at shortlisting stage, please ensure you clearly evidence in your application how you meet the essential and, where applicable, desirable criteria listed in the Candidate Information document on our website.

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