PhD Studentship: Learning and Decision-Making Under Uncertainty in Public Health University of Bristol in United Kingdom

[4-year University Scholarship starting in the 2027/28 academic year; minimum tax-free stipend at the current UKRI rate (2025/26 standard stipend £20,780); RTSG £8,400; full tuition fees covered.]

PhD Studentship in Learning and Decision-Making Under Uncertainty in Public Health at the University of Bristol.

This funded PhD project sits at the intersection of machine learning, statistics, and public health. The research will develop new methods for adaptive learning, sequential decision-making, and control in settings where data arrive over time and decisions affect future observations. Methodological themes include reinforcement learning, active learning, Bayesian decision theory, stochastic optimisation, probabilistic modelling, and network-based representations. The work is motivated by real-world problems in public health and epidemiology, such as adaptive disease surveillance, outbreak monitoring, resource allocation, and intervention planning using mobility and contact networks.

The studentship is a 4-year University Scholarship starting in the 2027/28 academic year. Funding includes a minimum tax-free stipend at the current UKRI rate, with the 2025/26 standard stipend listed as £20,780, plus RTSG £8,400 and full tuition fee coverage. The post is full time and is advertised as competitive worldwide funding.

The ideal applicant will have foundational knowledge of machine learning and strong self-motivation. The project is well suited to candidates interested in AI for dynamic systems, statistical decision-making, and computational methods for complex health-related applications. You will be supervised by Dr. Mengyan Zhang, whose research focuses on sequential decision-making and public health.

To apply, submit an online application through the University of Bristol postgraduate application portal, selecting the relevant programme and entering the studentship information in the funding and research sections. You must also email [email protected] with the subject line [PhD Application + Your name]. Required documents include a CV, personal statement, transcript, and optionally a research proposal and supporting academic or technical materials.

Applications close on 31 January 2027.