PhD Studentship: PhD Opportunity Title Philosophy of Automated Science

Over the last few years science has seen a rapid increase in talk about – and implementation of – automated workflows. This drive for more automation is in part fuelled by the emergence of powerful new AI agents that can operate as AI (co-) scientists. The promise of such AI-driven automation (ADA) is particularly prominent in laboratory-based disciplines, such as the life sciences.

However, the move towards ADA poses several epistemic and methodological challenges for science. The goal of this fully funded PhD Studentship is to use methods and resources from philosophy of science to investigate these challenges. A particular focus will be on the question of how the process of learning from error, which is central to scientific progress, might be affected by a move to ADA.

The PhD Studentship forms part of the Error-Reasoning Agents (ERAs) project, which aims to develop the tools required for assessing the error-reasoning ability of AI systems and to thereby foster the careful and effective introduction of AI agents into the research process.

The PhD candidate will be based at the Egenis Centre for the Study of the Life Sciences, a leading centre for interdisciplinary research on the biosciences and related disciplines. Egenis has a long tradition of pursuing a practice-focused approach to philosophy and offers a lively and diverse academic community that brings together philosophers, scientists, and social scientists.

Specific research tasks the candidate will pursue include the analysis of case studies from the biological sciences; engagement with current literature in philosophy of science and AI; as well as field work in bioscience laboratories at the University of Exeter. The student will also have funding available to travel to conferences to present and discuss their findings.

Suitable candidates will preferably have a background in both philosophy and the natural sciences, but the latter is not required. An interest in interdisciplinary communication and engagement as well as an understanding of how AI is currently deployed in science are desirable but not essential.

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