to verify in practice.
The successful candidate will undertake a project in the area of statistics/data science with the goal of (i) understanding the consequences of violations of core assumptions on the behaviour of Bayesian methods, and/or (ii) developing methods to mitigate these consequences. There will be a particular emphasis on applications from psychology, cognitive science and computer science, and the candidate will be encouraged to engage in interdisciplinary collaboration and communication.
We are looking for candidates with a background in or demonstrated ability to learn about: Bayesian methods, probabilistic machine learning or inverse modelling. Prospective applicants are encouraged to direct informal inquiries to Dr. Sabina Sloman (s.sloman@bham.ac.uk).
Keywords include: Bayesian experimental design, Bayesian transfer learning, Bayesian inverse problems, model misspecification, uncertainty quantification, generalised Bayesian inference, scientific applications
Funding Notes
The scholarship will cover home tuition fees, training support, and a stipend at standard rates for 3-3.5 years.
Self-funded students worldwide are welcome to apply.
Project supervisors
Dr Sabina Sloman
