About the Role
We are recruiting a Post Doctoral Research Associate (PDRA) to work within the Centre for Data Futures (CDF) at King’s College London, in close collaboration with Prof Yulan He (KCL computer science) and her NLP group.
The post is central to the NAVIGATE research programme, which investigates how different modes of AI uncertainty expression affect the quality of human reasoning – specifically the capacity for genuine engagement with views that differ from one’s own (what the programme terms “doxastic plasticity”). The programme develops and deploys LLM-based systems in morally loaded professional contexts, including healthcare, law and education, and asks: what scaffolding strategies preserve the kind of deliberative openness required for collective norm-refinement?
The PDRA will lead the design, implementation, and evaluation of the programme's computational experiments. This includes developing experimental interfaces built around large language models, designing prompting and fine-tuning strategies to vary how models express uncertainty, and building evaluation pipelines to measure the downstream effects on human reasoning. The PDRA will collaborate with Prof. Yulan He (KCL Institute for AI, NLP) on the technical pipeline.
The post is offered as a full-time 20-month contract (with possibility of extension, depending on further research funding), with a start date from the 01st January 2027.
You should hold a PhD in Computer Science, with expertise in Natural Language Processing, and have a demonstrated track record of research at the intersection of NLP and human-centred or social applications. Experience with LLM fine-tuning, uncertainty quantification, or evaluation of language model outputs is particularly desirable.
About You
Essential Criteria
- PhD in Computer Science (or thesis submitted with viva pending) or equivalent experience.
- Specialist knowledge in NLP or a closely related area of machine learning, with proven experience working with large language models: including fine-tuning, prompt engineering, and evaluation of LLM outputs.
- Experience or demonstrated interest in uncertainty quantification or the evaluation of uncertainty expression in language model outputs.
- Proven research experience, as evidenced by publications in peer-reviewed journals or conference proceedings.
- Demonstrated interest in, and ability to engage with, social science and humanities literature relevant to AI system design (e.g., ethics of AI, human-computer interaction, deliberative quality).
- A clear interest in developing publications on NLP and the social implications of LLM uncertainty expression.
Desirable Criteria
- Experience of human participant experimental design or evaluation studies.
- Familiarity with participatory methods or co-design approaches in the context of AI systems.
- Experience in developing and/or releasing open-access datasets.
- Experience in presenting research to non-specialist (e.g., policy or practitioner) audiences.
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