Fully Funded PhD in Medical Biostatistics, Causal Inference and Machine Learning
About the Project
Fully Funded PhD in Medical Biostatistics, Causal Inference and Machine Learning
Developing novel methods for causal inference using healthcare data
University of Limerick – School of Medicine, Ireland
Start Date: September 2026
Duration: 4 years
Funding: Fully funded PhD studentship
Project Overview
Applications are invited for a fully funded PhD in Medical Biostatistics, Causal Inference, and Machine Learning at the University of Limerick.
This PhD project will develop novel statistical and machine learning methods for estimating causal treatment effects using longitudinal electronic health records and observational healthcare data.
Research Areas
- Causal inference using observational healthcare data
- Target trial emulation
- Dynamic treatment regimes
- Machine learning for causal inference
- Doubly robust and debiased machine learning methods
- Heterogeneous treatment effect estimation
- Longitudinal data analysis
- Survival analysis and competing risks
- Propensity score methods and causal weighting
- Transportability and generalisability of findings
- Sensitivity analysis and robustness assessment
- Reproducible research and statistical computing
Funding and Support
- Fully funded 4-year structured PhD
- €25,000 annual tax-free stipend
- Full tuition fee
- Funding support for conferences, workshops, and specialist training
- Access to computing resources and research infrastructure
Candidate Profile
Applicants should hold, or expect to obtain before September 2026, a First Class or Upper Second Class Honours degree (or international equivalent) in a quantitative discipline such as Statistics, Biostatistics, Mathematics, Applied Mathematics, Data Science, Computer Science, Econometrics, Epidemiology, or a related quantitative field.
Essential requirements
- Strong quantitative and statistical background
- Experience with statistical programming (preferably R)
- Interest in causal inference and machine learning
- Strong analytical ability
- Ability to work independently and collaboratively
Desirable experience
- MSc in statistics, biostatistics, data science, epidemiology, or a related area
- Experience analysing observational or longitudinal data
- Knowledge of causal inference methods
- Familiarity with survival analysis or time-to-event modelling
- Interest in reproducible research and open-source software
Application Process
Applicants should submit a single PDF containing: Curriculum vitae (CV), Cover letter outlining motivation and research interests, Academic transcripts, Contact details for two academic referees.
Applications will be reviewed on a rolling basis until the position is filled.
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