PhD Studentship in Learning and Decision-Making Under Uncertainty for Public Health

This PhD studentship at the University of Bristol focuses on advancing machine learning and statistical techniques for decision-making under uncertainty, with applications in public health and epidemiology. The project will explore reinforcement learning, Bayesian decision theory, and stochastic optimization to address challenges in adaptive data acquisition, sequential intervention, and robust learning in dynamic systems. The research will leverage probabilistic modeling, network-based representations, and modern AI methods to create scalable and interpretable decision-making tools. These tools will be applied to real-world problems such as adaptive disease surveillance, outbreak monitoring, and resource allocation. The ideal candidate will work under the supervision of Dr. Mengyan Zhang, whose expertise spans sequential decision-making and public health research.

• Develop novel machine learning and statistical methods for adaptive learning and sequential decision-making under uncertainty.
• Design and implement algorithms for adaptive data acquisition strategies to maximize information gain under resource constraints.
• Apply reinforcement learning techniques to sequential intervention and control problems in public health.
• Develop robust learning methods that adapt to incomplete observations, changing environments, and distributional shifts.
• Combine probabilistic modeling, network-based representations, and modern AI methods for scalable and interpretable decision-making.
• Apply research findings to real-world applications such as adaptive disease surveillance, outbreak monitoring, and resource allocation.
• Collaborate with interdisciplinary teams to integrate findings into public health frameworks and policy recommendations.
• Present research findings at conferences and publish in leading academic journals.