Applications are invited for a Postdoctoral Research Associate in transportation data science and statistical modelling, based in the Centre for Transport Engineering and Modelling (CTEM) at Imperial College London, and contributing to the research programme of the Transport Strategy Centre (TSC). The post forms part of a funded research agenda aimed at advancing the scientific understanding and causal analytical foundations of mass transit system performance. The research is expected to contribute both novel methodological advances in statistical modelling, causal inference, and machine learning, and substantive empirical insights relevant to the design and operation of mass transit systems.
The primary objective of the role is to develop, extend, and apply rigorous statistical and computational methods for the modelling and causal analysis of mass transit systems. The research will focus in particular on the development and application of data science methods to address key challenges in capacity utilisation, service reliability, system resilience, accessibility, and decarbonisation pathways towards net-zero transport systems.
The postholder will undertake original research involving the development and application of statistical modelling, causal inference, and machine learning methods. The work will combine theoretical model development with the analysis of large-scale, high-dimensional transport datasets, drawing on computational approaches to identify causal mechanisms, estimate treatment effects, and derive data-driven insights into system behaviour and the impacts of operational interventions.
Applicants should hold a PhD (or be close to completion) in a relevant analytical and quantitative discipline, such as civil or transport engineering, transport modelling, mathematics, statistics, data science, computer science, or economics. The successful candidate will be expected to demonstrate the ability to conduct independent, high-quality research, to disseminate findings through publications in leading peer-reviewed journals and international conferences, and to contribute to the preparation of research reports and competitive research funding proposals
PhD (or near completion) in civil or transport engineering, transport planning, transport modelling, mathematics, statistics, data science, economics, computer science, or a closely related discipline, or equivalent research, industrial, or commercial experience. (E)
*Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant.
- Experience conducting analytical and numerical research relevant to mass transit, public transport, or transport system performance.(E)
- Knowledge of relevant analytical methods, including, but not limited to, statistical inference, causal inference and econometric methods (such as instrumental variables, regression discontinuity, and difference-in-differences), and machine learning methods, with familiarity with operations research and game theory as additional assets. (E)
- Ability to develop and apply new analytical concepts. (E)
- Advanced computer skills, including R or Python programming. (E)
The above list is not exhaustive. Applicants should review the full job description and criteria for this role.
- Professional development opportunities, including publishing in leading journals, presenting at international conferences, and shaping industry standards
- The opportunity to continue your career at a world-leading institution and be part of our mission to continue science for humanity.
- Grow your career: gain access to Imperial’s sector-leading dedicated career support for researchers as well as opportunities for promotion and progression.
- Sector-leading salary and remuneration package (including 39 days off a year and generous pension schemes).
- Be part of a diverse, inclusive and collaborative work culture with various staff networks and resources to support your personal and professional wellbeing.
If you have any questions about the role please contact: Professor D Graham [email protected]
Our preferred method of application is online via our website. Please click ‘apply’ below or go to https://www.imperial.ac.uk/job-applicants/and search using reference number ENG03959
Application enquiries should be directed to Alexandra Williams, [email protected]
- Closing Date: Midnight 14 July 2026
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