Area
Computer Science
Location
Jubilee Campus
Salary
£32,080 to £38,784
Closing Date
Thursday 23 July 2026
Reference
SCI146526
We are looking to recruit a postdoctoral researcher to join the growing Lab for Lab for Uncertainty in Data and Decision Making (LUCID) in the School of Computer Science. Applicants should have a relevant background in information extraction, entity resolution/entity linking, machine learning, uncertainty modelling, explainable AI, or a closely related area, with a PhD (or near completion) in computer science, artificial intelligence, data science, computational linguistics, or another relevant subject area with the required expertise.
You, the successful applicant, will contribute to interdisciplinary research on uncertainty-aware entity resolution and explainable decision support. Specifically, you will work on methods for capturing, representing, propagating and aggregating multiple forms of uncertainty in entity resolution pipelines, including uncertainty associated with entity spans, entity types, contextual evidence, model support, source provenance and cross-source conflict.
You will be part of the project EXERCURA: Explainable Entity Resolution via Multi-Component Uncertainty and Robust Aggregation, between the University of Nottingham, The Alan Turing Institute. As a member of the research team, you will be based within the LUCID research group at the University of Nottingham’s Jubilee Campus, with opportunities for collaboration with relevant partners and stakeholders.
The work has a strong focus on developing explainable and uncertainty-aware AI systems for linking mentions to entities under ambiguity, while building on advances in natural language processing, representation learning, information fusion, robust aggregation and human-centred decision support. A key part of the project is to develop approaches that help users understand not only which entity has been linked, but also why a link was made, why alternatives may remain plausible, or why a case may need to be deferred for analyst review.
You will have expertise in areas such as natural language processing, entity resolution/entity linking, information extraction, machine learning, representation learning, uncertainty estimation, explainable AI, or information aggregation/fusion, together with strong programming skills. You will be keen to innovate and excited to learn more about areas which you may not yet be familiar with, including from other disciplines, integrating insights into your research and associated publications. A prior background in interdisciplinary research, human-centred AI, decision support, or analyst-facing systems would be desirable but is not essential.
Your key activities will be to design and implement methods for decomposing and capturing uncertainty signals in entity resolution. You will also develop and evaluate uncertainty-aware aggregation approaches, including methods that combine evidence-worth and source-worth to support robust and inspectable entity linking. The project will include software implementation, experimental evaluation, contribution to project deliverables, and the writing-up and dissemination of research outputs, including presentation at conferences as appropriate.
You will be mentored throughout the duration of the role, and the research team will support you in developing your research career.
The project is not currently classified and formal security clearance is not required for this role at this stage. The successful applicant may be required to complete a Personal Particulars – Research Workers form and meet the UK Government Baseline Personnel Security Standard. Future project activities may be subject to additional security screening or formal security clearance requirements.
What next
Further information is available in the role profile. To apply for this vacancy please click ‘Apply Now’ to complete your details.
To find out more about what we can offer you, follow the link to our benefits website
This is a Full Time, Fixed-Term post until 31/03/2027.
Informal enquiries may be addressed to Dr Direnc Pekaslan at [email protected]. Please note that applications sent directly to this email address will not be accepted.
