PhD Studentship: Robust Bayesian Experimental Design and Inference
University of Birmingham
Statistical methods are leveraged in many scientific applications to: specify a data collection practice (experimental design), draw conclusions from sparse and noisy data (inference), and assess uncertainty about those conclusions (uncertainty quantification). Bayesian inference and experimental design methods are increasingly used in scientific practice, and offer appealing theoretical guarantees when certain assumptions hold. However, whether these assumptions hold is difficult or impossible to verify in prac...
Statistical methods are leveraged in many scientific applications to: specify a data collection practice (experimental design), draw conclusions from sparse and noisy data (inference), and assess uncertainty about those conclusions (uncertainty quantification). Bayesian inference and experimental design methods are increasingly used in scientific practice, and offer appealing theoretical guarantees when certain assumptions hold. However, whether these assumptions hold is difficult or impossible to verify in prac...
Opportunity details
Statistical methods are leveraged in many scientific applications to: specify a data collection practice (experimental design), draw conclusions from sparse and noisy data (inference), and assess uncertainty about those conclusions (uncertainty quantification). Bayesian inference and experimental design methods are increasingly used in scientific practice, and offer appealing theoretical guarantees when certain assumptions hold. However, whether these assumptions hold is difficult or impossible 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). 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.
Eligibility
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.
Requirements
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.
Funding and benefits
Funding eligibility: UK Students, Self-funded Students. Funding amount: The scholarship will cover home tuition fees, training support, and a stipend at standard rates for 3-3.5 years.. 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.
How to apply
Statistical methods are leveraged in many scientific applications to: specify a data collection practice (experimental design), draw conclusions from sparse and noisy data (inference), and assess uncertainty about those conclusions (uncertainty quantification). 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. Self-funded students worldwide are welcome to apply.
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Recorded source check: September 4, 2026. A stored check date does not guarantee that every field is complete or still current. Compare the provider, eligibility, funding and deadline with the live official call before applying.
Benefits
Funding eligibility: UK Students, Self-funded Students. Funding amount: The scholarship will cover home tuition fees, training support, and a stipend at standard rates for 3-3.5 years.. 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.
Eligibility summary
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.
Requirements
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.
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