As Research Fellow (Artificial Intelligence) you will assist in cutting-edge theoretical research on the causal metaphysics of Bayesian networks. You will use causal Bayesian networks to analyse current deterministic and stochastic accounts of actual (token) causation and assist in developing new stochastic analyses. The relation between stochastic token causation and type causation will be investigated, with a view to integrating these accounts with causal information theory. The outcomes of the project will include an information-theoretic analysis of causal explanation that allows for distinct degrees of explanatory strength.
1. A PhD in Artificial Intelligence, Philosophy of Science, Mathematics or a related field. 2. Expertise in Bayesian network technology. 3. A good understanding of current work in causal Bayesian networks, causal metaphysics, information theory, experimental philosophy and philosophy of science; a practical understanding of computer programming and statistics. 4. A proven ability to pursue original research, including a track-record of research publications. 5. Good communication skills, both written and oral.