Summary
The University of Limerick is inviting applications for a fully funded 4-year PhD position in Statistics, Causal Inference and Machine Learning within the School of Medicine. The research project focuses on developing novel statistical methods for estimating causal treatment effects using longitudinal electronic health records and observational healthcare data.
The successful candidate will work at the intersection of causal inference, statistical methodology, machine learning, and health data science, addressing challenges such as dynamic treatment strategies, time-varying confounding, treatment-effect heterogeneity, and complex survival outcomes.
PhD Scholarship in Statistics, Causal Inference & Machine Learning at University of Limerick, Ireland
Designation
PhD Researcher – Statistics, Causal Inference & Machine Learning
Research Area
Causal Inference, Machine Learning, Statistical Methodology, and Health Data Science for Healthcare Analytics.
- Causal Inference
- Semiparametric Statistics
- Machine Learning for Causal Inference
- Target Trial Emulation
- Dynamic Treatment Regimes
- Longitudinal Data Analysis
- Survival Analysis & Competing Risks
- Health Data Science
- Electronic Health Records (EHRs)
- Statistical Computing and Open-Source Software Development
Location
School of Medicine, University of Limerick, Ireland
Start Date: September 2026
Scholarship/Funding Details
| Funding Component | Details |
|---|---|
| Scholarship Type | Fully Funded 4-Year Structured PhD |
| Stipend | €25,000 per year (tax-free) |
| Tuition Fees | EU tuition fees covered |
| Research Support | Conference, training and workshop funding |
| Additional Support | Laptop provided |
Project Overview
The PhD project aims to develop advanced statistical methodologies for causal inference using large-scale healthcare datasets. The research will address key challenges in observational healthcare data analysis, including:
- Dynamic treatment strategies
- Time-varying confounding
- Treatment-effect heterogeneity
- Limited treatment overlap
- High-dimensional longitudinal data
- Competing risks and complex survival outcomes
The research applications will support healthcare decision-making in areas such as:
- Medication optimisation
- Deprescribing strategies
- Cardiovascular disease prevention
- Diabetes management
- Mental health interventions
- Cancer prevention and screening
- Clinical decision support
- Palliative care
Job Description / Research Responsibilities
The selected PhD researcher will:
- Develop innovative statistical methods for causal inference using healthcare data.
- Work on semiparametric and machine learning-based estimation approaches.
- Analyse longitudinal electronic health records and observational datasets.
- Apply methods including:
- Target trial emulation
- Doubly robust estimation
- Double/debiased machine learning
- Propensity score methods
- Overlap weighting
- Sensitivity analysis
- Transportability and generalisability methods
- Conduct reproducible statistical research and develop open-source software tools.
- Publish research findings in leading journals.
- Present work at international conferences and research workshops.
Data Sources
The research may involve:
- UK Biobank
- Longitudinal electronic health records
- Linked routine healthcare datasets
- Population-based cohort studies
- Simulated datasets
Eligibility / Qualification
Essential Requirements
Applicants must have or expect to obtain before September 2026:
- 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
- Related fields
Additional requirements:
- Strong quantitative and statistical background.
- Experience with statistical programming (preferably R).
- Strong interest in causal inference and statistical methodology.
- Excellent communication skills.
- Ability to work independently and collaboratively.
Desirable Qualifications
- MSc in a relevant discipline.
- Experience with observational or longitudinal data.
- Knowledge of causal inference or machine learning methods.
- Familiarity with survival analysis or time-to-event modelling.
- Interest in reproducible research and open-source development.
Research Training & Benefits
The candidate will receive training in:
- Modern causal inference and statistical learning.
- Semiparametric and high-dimensional statistics.
- Longitudinal and survival data analysis.
- Scientific computing and reproducible research.
- Academic writing and research communication.
The scholar will also have opportunities to:
- Publish in international journals.
- Attend specialist workshops and summer schools.
- Present research at global conferences.
- Collaborate with experts in statistics, medicine, and health data science.
Supervisor
Dr Maurice O’Connell
Associate Professor in Medical Biostatistics
School of Medicine, University of Limerick
Additional methodological and clinical collaborators may contribute to supervision and training.
How to Apply
Applicants must submit a single PDF document containing:
- Cover letter explaining motivation and research interests.
- Curriculum Vitae (CV).
- Academic transcripts.
- Contact details of at least two academic referees.
Applications should be submitted through the official application process mentioned in the EURAXESS advertisement.
Informal enquiries:
Dr Maurice O’Connell
School of Medicine, University of Limerick
Email: maurice.oconnell@ul.ie
Last Date to Apply
Applications are reviewed on a rolling basis until the position is filled.
Only shortlisted candidates will be contacted.
Further Information: EURAXESS Advertisement: https://euraxess.ec.europa.eu/jobs/445195
