Fully Funded PhD in Statistics, Causal Inference and Machine Learning at University of Limerick
Fully funded PhD opportunity in
Statistics, Causal Inference and Machine Learning
at the
School of Medicine, University of Limerick
, Ireland.
The project focuses on developing new statistical methods for causal inference using longitudinal electronic health records and observational healthcare data. Research themes include target trial emulation, dynamic treatment regimes, heterogeneous treatment effects, semiparametric and nonparametric estimation, doubly robust methods, debiased/double machine learning, longitudinal data analysis, survival analysis, competing risks, propensity score methods, transportability, sensitivity analysis, and reproducible statistical computing.
The successful candidate will join an interdisciplinary research environment and work on methodological questions motivated by multimorbidity, polypharmacy, chronic disease management, and other healthcare applications. Applications are especially encouraged from students interested in statistical methodology, causal inference, semiparametric statistics, machine learning, and health data science.
Funding includes a 4-year fully funded PhD, a €25,000 annual tax-free stipend, tuition fees covered, and conference, training, workshop, and laptop support.
Applicants should have, or expect to obtain before September 2026, a First Class or Upper Second Class Honours degree or equivalent in a quantitative discipline such as Statistics, Biostatistics, Mathematics, Applied Mathematics, Data Science, Computer Science, Econometrics, Epidemiology, or a closely related field. Strong quantitative skills, statistical programming experience preferably in R, and a strong interest in causal inference and statistical methodology are required.
Applications are reviewed on a rolling basis until the position is filled. To apply, prepare a single PDF with a cover letter, CV, academic transcripts, and referee contact details. Informal enquiries may be sent to Maurice O'Connell at [email protected].