PhD in Distributed Optimisation

How can networks of sensors work collectively in contested environments? This project develops distributed signal processing and filtering algorithms enabling autonomous systems to fuse information reliably under uncertainty and communication constraints. Working with DSTL, you will design principled, scalable methods for next-generation defence sensing and decision-support systems./nModern defence systems rely on networks of heterogeneous sensors operating across platforms, domains, and communication constraints. Extracting reliable situational awareness from such complex integrated systems requires new distributed signal processing and filtering algorithms that are scalable, robust, and mathematically principled./nThe research will develop probabilistic models and distributed Bayesian filtering frameworks that explicitly account for uncertainty, communication constraints, decentralised computation, and resilience. Key themes include multi-sensor data fusion, distributed optimisation, information-aware filtering, and robust decision-making under uncertainty./nIntended outcomes include new algorithmic architectures for distributed inference, theoretical guarantees on performance and robustness, and validated prototypes demonstrated on defence-relevant sensing scenarios./nLocation: University of Southampton, Southampton, United Kingdom.