PhD Studentship: Reinforcement Learning for Sequential Disease Surveillance and Control

Funding amount

Standard UKRI stipend, RTSG, Fees Covered.

The project: 

Tracking and controlling the spread of emerging pathogens using sequentially collected data is a critical challenge in public health. Policymakers allocate limited testing and surveillance resources across different locations, aiming to maximise the information gained about disease prevalence and incidence. This project will develop AI-driven methods for optimising disease surveillance and intervention strategies in dynamic, real-world settings.

We will design an active disease surveillance and control framework based on a dynamic mobility network to better capture the complexities of disease spread. The project focuses on two core objectives:

1. Adaptive surveillance: designing policies that actively allocate testing resources to maximise early outbreak detection.
2. Active intervention: developing decision-making algorithms that recommend effective public-health interventions.

Reinforcement learning (RL) provides a natural framework for these tasks, enabling decision-making under uncertainty and the optimisation of long-term outcomes, such as reducing infections or fatalities. As new data are collected, RL algorithms will update their policies, allowing the system to adapt to changing conditions and optimise strategies in real time. This iterative approach aims to improve the efficiency of resource allocation and help policymakers implement more effective interventions based on up-to-date predictions.

The ideal candidate will have foundational knowledge of machine learning and strong self-motivation. You will be supervised by Dr. Mengyan Zhang (https://mengyanz.github.io/), whose research focuses on sequential decision making and public health. Dr. Zhang has published in leading venues including Nature, PNAS, ICML, AAAI, etc. She collaborates widely through the Machine Learning and Global Health network, including with researchers at the University of Oxford, Imperial College London, and the University of Copenhagen.  

How to apply:

Prior to submitting an online application, you will need to contact the project supervisor to discuss. 

Online applications click the 'Apply' button. Please select Computer Science on the Programme Choice page. You will be prompted to enter details of the studentship in the Funding and Research Details sections of the form. 

Candidate requirements: 

Applicants must hold/achieve a minimum of a merit at master’s degree level (or international equivalent) in a Science, Mathematics or Engineering discipline. Applicants without a master's qualification may be considered on an exceptional basis, provided they hold a first-class undergraduate degree. Please note, acceptance will also depend on evidence of readiness to pursue a research degree. 

If English is not your first language, you need to meet this profile level: Profile E

Further information about English language requirements and profile levels.

Funding: University Scholarship. Minimum tax-free stipend at current UKRI rate is £20,780 for 2025/26 (will increase each year), plus full tuition fee and £2100 RTSG per annum.

Contacts:

For questions about the research topic, please contact Dr Mengyan Zhang - mengyan.zhang@bristol.ac.uk. 

For questions about eligibility and the application process please contact Engineering Postgraduate Research Admissions admissions-engpgr@bristol.ac.uk

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