Scalable Intelligent Planning for Partially Observable Uncertain Systems

The stability of modern life builds on sustainable, continuous operation of large-scale infrastructure, such as power grids, water networks, and transportation systems. However, managing these "systems of systems" presents a profound scientific and logistical challenge that remains largely unsolved.

These systems are complex, nonlinear or sometime stochastic, and this means small local failures can trigger massive cascading effects. These systems are typically at a very large scale consisting different subsystems with similar or coherent functions in various specifications. Regardless, the status monitoring of such systems is very challenging, because only partial data is observable at variant frequency associated with significant uncertainties.

Motivated to effectively maintain the functionality, it is important to carry out inspection and maintenance at the right time at an appropriate scale. In particular, every repair action is a high-stakes trade-off, i.e., maintenance requires time and resources while temporarily reducing system availability. There is not yet a scalable system level methodology that allow for adaptively prioritise the actions for these complex uncertain systems.

We are seeking PhD applicants with a strong academic track record in mathematics, engineering, or computer science. Successful candidates will possess the analytical rigor to tackle nonlinear modelling and the drive to apply theoretical breakthroughs solving the challenge in operating infrastructure sustainably.