[School of Natural Sciences PhD Scholarships] Bayesian optimal experimental design

About the project

About the Project In recent years, there has been an explosion of interest in Bayesian Optimal Experimental Design (BOED) (e.g. Rainforth et al., 2024), a statistical field that has benefitted from an influx of ideas from machine learning. The core idea of BOED is to identify experimental conditions that maximise the information gained from the data collected, as measured by the expected reduction in entropy from the prior distribution to the posterior, thereby reducing the amount of data required while improving the quality of statistical inference. These methods have the potential to improve decision-making and data collection across a wide range of scientific disciplines, including chemistry, psychology, and the development of new medicines. This project will focus on the development of new methodology for BOED. Possible research directions include robustness to model misspecification, scalable algorithms for BOED, and methods for sequential design problems. There is considerable flexibility for the project to be adapted to the student's strengths and interests, as well as to the latest developments in this rapidly evolving field. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Natural Sciences Scholarships . If you don’t already have an applicant account, please follow the instructions here . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing your motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility : The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree (or international equivalent) in Mathematics, Statistics, Computer Science, Machine Learning, or Data Science OR any upper-second class (2:1) honours degree and a Master’s degree at merit (or international equivalent) in Mathematics, Statistics, Computer Science, Machine Learning, or Data Science. Applicants should have a strong background in probability and statistics, ideally including Bayesian statistics, Monte Carlo methods, and statistical computing. Experience in machine learning, optimisation, or modern computational frameworks (e.g. PyTorch) would be advantageous. This project will remain open until filled. If your application is submitted by 1st November 2026, you can expect a decision by 18th December 2026. If your application is submitted by 15th January 2027, you can expect a decision by 30th March 2027. Self or externally funded students can also be considered for this project. FSESoNS

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