Research Fellow in Causal Inference and Data-Driven Urban Modelling

Post Status: Specific Purpose (Full-time) 

Research Group / Department / School: Transportation Engineering, Department of Civil Engineering, Trinity College Dublin, the University of Dublin 

Location: Simon Perry Building, Trinity College Dublin, the University of Dublin College Green, Dublin 2, Ireland 

Reports to: Prof Brian Caulfield 

Salary: This appointment will be made on IUA Research Fellow Salary Scale (Level 1) ( of €66,548 per annum) 

Terms & Conditions: Provided below 

Hours of Work: Full-time 

Closing Date: 12 Noon (GMT), 1st May, 2026

Post Summary 

The Research Fellow will play a key role in the SEAI-funded CIRCUIT project (Causal Inference for Resilient Cities and Urban Integrated Transport) at Trinity College Dublin. The project develops an integrated modelling framework combining causal machine learning, transport behaviour analysis, and residential energy demand modelling to support sustainable urban and energy policy. 

The researcher will contribute to the design, implementation, and validation of causal inference models, working closely with the Principal Investigator, PhD student(s), and external stakeholders. The role includes methodological development, empirical analysis using Irish and international datasets, and the production of high-quality academic and policy outputs.

Funding Information 

This research position is supported by the Sustainable Energy Authority of Ireland (SEAI) under the National Energy Research, Development and Demonstration Programme as part of the CIRCUIT project. A salary of €66,548 per annum will be provided for four years, subject to satisfactory performance. A one-time fund for a laptop/PC for research use will be provided. Funding may also be provided to attend one international and one EU conference per year, subject to satisfactory performance.

Essential Qualifications / skills / knowledge 

• At least 3 years post-doc experience 

• A PhD in transport engineering, economics, data science, statistics, geography, computer science, or a closely related discipline. 

• Track record of publications in top journals 

• Excellent communication and writing skills in English 

• Prior experience in project management in transport, energy, sustainability, or quantitative social science. 

• Strong foundation in mathematics and statistics (quantitative or analytical background) 

• Familiarity with statistical analysis or programming (e.g. Python, R, MATLAB, AcrGIS/QGIS, Biogeme) 

• Experience working with large datasets or survey data