Postdoctoral Research Fellow Full-time NEW

Postdoctoral Research Fellow

The Stringhini Lab at the UBC School of Population and Public Health is seeking a full-time (1.0 FTE) Postdoctoral Research Fellow for a one-year (renewable) appointment. The individual will have outstanding quantitative skills to lead analyses on our ageing research program. The successful candidate will work at the intersection of life-course epidemiology, causal inference, and artificial intelligence to advance our understanding of the biological pathways linking social exposures to healthy and unhealthy ageing. This is a highly autonomous, intellectually stimulating position suited for a researcher who thrives on methodological innovation and collaborative science.

The ideal applicant will possess a curiosity and passion for science, as well as deep expertise in quantitative epidemiological methods, statistical modelling, and causal inference. Accurate attention to detail is essential.

This position reports to the lead Principal Investigator Dr. Silvia Stringhini (School of Population and Public Health and Edwin S.H. Leong Centre for Healthy Ageing). The position will support students and other staff members.

The Stringhini Lab at the UBC School of Population and Public Health investigates the social and environmental determinants of health across the life course, with a particular focus on biological embedding and biological ageing. We use worldwide multi-cohort data to examine how life-course socioeconomic conditions become biologically embedded, as measured by epigenetic clocks, allostatic load, telomere length, and other biomarkers of ageing. Our methodological approach is strongly quantitative and causally oriented, integrating causal inference frameworks (directed acyclic graphs, g-computation, propensity score methods, instrumental variables, and natural experiments), counterfactual mediation analysis in its various forms (natural direct and indirect effects, interventional effects, multiple mediation), and advanced longitudinal modelling (mixed-effects models, growth curve models, latent class trajectories) with machine learning and AI-based approaches. The Lab is embedded within the Edwin S.H. Leong Centre for Healthy Ageing and benefits from a rich international network of collaborators.

Housed within the Faculty of Medicine, the School of Population and Public Health (SPPH) is an innovative unit that encompasses many of the health-related groupings at UBC as a collaborative venture. The School is structured around four divisions: Global and Environmental Health; Health Services and Policy; Epidemiology, Biostatistics and Public Health Practice; and Health in Populations. The resulting mix of professions and disciplines is seen as a means of connecting individuals and learners to galvanize the relationship between health research, public health and health services and to enhance learning.

Responsibilities

Reporting to Dr. Stringhini, the incumbent will be responsible for:

• Leads statistical analyses on multi-cohort longitudinal data, focusing on social determinants of biological ageing outcomes and the mediating role of concurrent factors.
• Develops and applies causal inference frameworks — including counterfactual mediation analysis in its various forms (natural direct and indirect effects, interventional effects, multiple mediation) — to ageing research questions.
• Implements and adapts machine learning and AI-based approaches for high-dimensional epidemiological data, including variable selection, prediction modelling, and data integration.
• Retrieves, harmonizes, and preprocesses multi-cohort longitudinal datasets to ensure comparability across different study designs and measurement structures, ensuring reproducibility and adherence to FAIR data principles.
• Leads and co-authors peer-reviewed manuscripts; presents findings at national and international conferences.
• Contributes to ongoing strategic planning for the lab's epidemiological analysis needs; collaborates with the principal investigator to identify funding opportunities and develop grant proposals.
• Mentors graduate students and junior lab members.
• Participates actively in lab meetings, journal clubs, and collaborator calls.
• Maintains and develops up-to-date knowledge of current information technology techniques and tools, especially as they apply to epidemiological analysis.
• Other related duties as required.

Qualifications

Successful applicants will have:

• Completed PhD within 5 years in a relevant discipline (epidemiology, biostatistics, statistics, bioinformatics, or a closely related quantitative discipline). Candidates who have submitted but not yet defended are eligible to apply.
• Demonstrated expertise in causal inference methods (e.g., counterfactual mediation, propensity scores, DAGs, g-formula, instrumental variables, natural experiments).
• Strong experience with mediation analysis in its various forms, including traditional and causal (counterfactual) mediation, natural direct and indirect effects, interventional effects, and multi-mediator (multiple mediation) approaches.
• Experience with machine learning / AI methods applied to health or omics data.
• Advanced proficiency in R and/or Python; experience with reproducible workflows (Git/GitHub, R Markdown/Quarto).
• Extensive experience working with large-scale longitudinal or multi-cohort datasets, preferably from population cohort studies.
• Familiarity with biological ageing markers (epigenetic clocks, allostatic load, telomere length) is an asset.
• Experience with omics data integration (epigenomics, metabolomics, proteomics) is an asset.
• Knowledge of data harmonization platforms (e.g., Maelstrom Research guidelines) is an asset.
• Experience with high-performance computing environments (Unix/HPC clusters) is an asset.
• Strong publication records commensurate with career stage.
• Strong verbal and written communication skills in English.
• Ability to develop and maintain cooperative and productive working relationships.
• Ability to adapt to changing priorities and work effectively under pressure to meet deadlines.
• Must conduct all activities ethically and maintain the confidentiality of funding information and research data.
• Willingness to respect diverse perspectives, including perspectives in conflict with one's own.
• Demonstrates a commitment to enhancing one's own awareness, knowledge, and skills related to equity, diversity, and inclusion.