PhD Studentship: A Novel Control Framework for Embodied Decision-Making in Multi-Robot Coordination and Task Execution The University of Manchester in United Kingdom

[£20,780 annual tax-free stipend (for 2025/26) plus full tuition fees for 3.5 years. Stipend expected to increase each year. Available to home students and EU students with settled status.]

This fully funded 3.5-year PhD studentship at The University of Manchester offers an exciting opportunity to advance the field of multi-robot coordination and embodied decision-making. The project is designed for highly motivated candidates with a strong background in control theory and practical robotics experience. Successful applicants will receive a competitive annual tax-free stipend of £20,780 (for 2025/26), with full tuition fees covered, and the stipend is expected to increase each year. Funding is available to home students and EU students with settled status.

The research focuses on developing a novel control framework that enables autonomous robotic swarms to operate with resilience, reliability, and adaptability in unknown, dynamic, and unstructured environments. Such swarms are increasingly vital for complex missions like search-and-rescue and large-scale surveillance, where single robots are insufficient. The project investigates how individual and collective decision processes emerge from the dynamical sensorimotor loops of physical systems, emphasizing the tight coupling between body dynamics, sensing, and agent-environment interactions.

Adopting an interdisciplinary approach, the work combines control theory, nonlinear dynamical systems, robotics, and formal methods to create principled models and algorithms for distributed decision-making in complex settings. The candidate will develop a hierarchical control framework integrating cognitive decision-making with physical systems control, focusing on two main components: low-level safe coordination control and high-level decision-making and task coordination. The low-level safety layer ensures efficient and safe navigation of robot swarms under complex dynamics, while the high-level layer leverages collective decision-making mechanisms, such as opinion dynamics, to coordinate multi-agent behaviors in response to changing internal and external conditions.

Both traditional model-based and modern learning-based control techniques will be explored to balance reliability, performance, computational efficiency, and adaptability under uncertainty. The project is affiliated with CRADLE (Center for Robotic Autonomy in Demanding and Long-Lasting Environments), providing access to collaborative research, formal verification techniques, and hands-on demonstrations. The candidate will work closely with CRADLE’s Work Package 2 (Architectures) and Work Package 5 (Demonstrators), benefiting from a supportive research environment and opportunities for interdisciplinary collaboration.

Ideal applicants should hold, or expect to obtain, at least an Upper Second-Class Honours degree (2:1) or a Master’s degree (or international equivalent) in Control Engineering, Robotics, Applied Mathematics, or a related quantitative discipline. Research experience in control theory or robotics is highly desirable, and prior exposure to robotic platforms, including hands-on experience with ROS, will be considered an advantage. Enthusiasm for both theoretical development and practical implementation is essential.

To apply, contact the main supervisor, Dr Zhiqi Tang, at [email protected], providing details of your academic background, current level of study, relevant experience, and a paragraph outlining your motivation for this PhD project. Early application is recommended, as the advert may be removed before the official deadline. Formal applications should be submitted via the university’s application portal using the provided link.