PhD fellowship in Biostatistics and causal inference for clinical trials at the SMARTbiomed Pioneer centre

This PhD fellowship position in Biostatistics and causal inference for clinical trials at the SMARTbiomed Pioneer centre requires advanced knowledge and skills in biostatistics, with a strong emphasis on causal inference methodologies applied to clinical trial data. Candidates should have a solid foundation in statistical theory and practice, including expertise in designing, analyzing, and interpreting clinical trial results. Proficiency in statistical programming languages such as R, SAS, or Python is typically essential for conducting complex data analyses and simulations. Familiarity with causal inference frameworks, such as potential outcomes, directed acyclic graphs (DAGs), instrumental variables, propensity score methods, and mediation analysis, is critical to address challenges in estimating treatment effects and understanding causal relationships in clinical research. The role demands strong quantitative skills, critical thinking, and the ability to apply rigorous statistical methods to real-world biomedical problems. Experience or coursework in clinical trial design, survival analysis, longitudinal data analysis, and regulatory requirements for clinical research would be advantageous. Candidates are expected to engage in interdisciplinary collaboration, communicate statistical concepts effectively to clinical researchers, and contribute to scientific publications. A background in mathematics, statistics, biostatistics, epidemiology, or a related quantitative discipline is necessary, with a demonstrated interest or prior experience in causal inference and clinical trial methodology. This fellowship is suitable for candidates aiming to develop expertise at the intersection of biostatistics and causal inference within the biomedical research context, preparing them for academic or industry roles focused on innovative clinical trial analysis.