Postdoctoral Research Associate

Updated: about 1 hour ago
Location: London, ENGLAND
Deadline: 20 Apr 2026

About the Role

Applications are invited for a 30-month, part-time (0.5 FTE) Postdoctoral Research Associate to support DigiWeB (Digital Welfare Borders), an ERC-funded project investigating how artificial intelligence shapes welfare governance and migration management across Europe. The role is jointly based in the School of Society and the Environment and the School of Electronic Engineering and Computer Science at Queen Mary University of London, working with Professor Rachel Humphris and Dr Dimitrios Kollias.

The successful applicant will undertake computational research, including algorithmic auditing, explainable AI (XAI) analysis and data-flow mapping, on welfare-AI systems in the UK, Netherlands and Sweden, contributing to comparative insights and high-quality project outputs. They will collaborate across disciplines, maintain secure datasets and participate in the wider research community, including Queen Mary’s Digital Environment Research Institute (DERI).

About You

The successful applicant will hold a PhD (or close to completion) or possess equivalent research experience in a relevant computational field such as data science, artificial intelligence, machine learning, computer science or statistics. They will bring strong analytical and programming skills, with experience in techniques such as algorithmic auditing, explainable AI (XAI), model evaluation, or reverse engineering of predictive systems. They will be confident working with large or complex datasets, ensuring accuracy, reproducibility and secure data management.

They will be able to work both independently and collaboratively as part of an interdisciplinary team, engaging effectively with colleagues across technical and social-scientific domains. They will be organised, methodical and able to communicate complex computational concepts clearly to wider audiences. An understanding of data governance, privacy and ethical considerations in AI systems is essential, alongside a commitment to maintaining high research standards throughout the project. 

About the School/Department/Institute/Project

The School of Environment and Society which is nested within the Faculty of Humanities and Social Sciences. With 150 academic staff, the School is a vibrant inclusive community where disciplines come together to explore and challenge our understanding of the world. The Department of Sociology, Politics and International Relations is internationally recognized as a world-leading centre of theoretically informed, empirically-grounded and politically-engaged research. The School of Electronic Engineering and Computer Science is firmly established as a centre of national and international excellence in research. As a multidisciplinary school, the School is well known for its pioneering research and world-class interdisciplinary research projects.

About Queen Mary

At Queen Mary University of London, we believe that a diversity of ideas helps us achieve the previously unthinkable.

Throughout our history, we’ve fostered social justice and improved lives through academic excellence. And we continue to live and breathe this spirit today, not because it’s simply ‘the right thing to do’ but for what it helps us achieve and the intellectual brilliance it delivers.

Benefits

We offer competitive salaries, access to a generous pension scheme, 30 days’ leave per annum (pro-rata for part-time/fixed-term), a season ticket loan scheme and access to a comprehensive range of personal and professional development opportunities. In addition, we offer a range of work life balance and family friendly, inclusive employment policies, campus facilities and flexible working arrangements.

Queen Mary’s commitment to our diverse and inclusive community is embedded in our appointments processes. Reasonable adjustments will be made at each stage of the recruitment process for any candidate with a disability. We are open to considering applications from candidates wishing to work flexibly.



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