PhD Studentship: Robust Bayesian Experimental Design and Inference
Investigate the robustness of Bayesian methods and develop new techniques to mitigate assumption violations in scientific applications.
Best for:Best for students with a strong foundation in statistics or data science and an interest in Bayesian methodology and interdisciplinary research.
Overview
The University of Birmingham's School of Mathematics is offering a PhD studentship focused on the intersection of statistics, data science, and scientific application. The research aims to evaluate how violations of core assumptions impact Bayesian methods and to develop robust alternatives for reliable inference. The project will emphasize interdisciplinary applications, particularly within psychology, cognitive science, and computer science. The successful candidate will work under expert supervision to advance theoretical guarantees in Bayesian experimental design and uncertainty quantification. Applicants are expected to engage in collaborative research and communicate findings across these diverse fields.
What you'll do
- Conduct original research on the robustness of Bayesian methods under assumption violations.
- Develop novel statistical or computational methods to mitigate the impact of assumption failures.
- Apply Bayesian experimental design techniques to problems in psychology and cognitive science.
- Collaborate with researchers across mathematics, computer science, and psychology departments.
- Communicate research findings through academic publications and presentations.
- Participate in departmental seminars and training activities.
Requirements
Degree required: Bachelor's or Master's degree in Mathematics, Statistics, Computer Science, or a related quantitative field.
Degree offered: PhD
Language: English proficiency is required for research and communication.
Instruction: English
- Strong background in Bayesian methods or probabilistic machine learning.
- Demonstrated ability to perform mathematical or statistical research.
- Proficiency in programming for data analysis and modelling.
- Capacity to learn complex inverse modelling techniques.
- Open to home students (funding covers home fees).
- Self-funded international students are welcome to apply.
Funding & terms
How to apply
Deadline: 2026-12-04
Interested candidates should review the application requirements on the university's official postgraduate study portal. Informal inquiries regarding the project should be directed to Dr. Sabina Sloman via email. Applications must be submitted through the official university online application system.
- CV
- Academic transcripts
- Research statement
- Reference letters
Contact
Sabina Sloman· Supervisor
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