This PhD project at The University of Manchester focuses on developing a novel control framework for embodied decision-making in multi-robot coordination and task execution. As autonomous unmanned systems become increasingly prevalent in real-world applications, the need for robust and adaptive robotic swarms is critical for complex missions such as search-and-rescue and large-scale surveillance. These environments are often unknown, dynamic, and unstructured, requiring swarms to adapt, select appropriate actions, and respond to changing conditions with resilience and reliability. The research aims to create a hierarchical control framework that tightly integrates cognitive decision-making with physical systems control. The framework will feature two main components: low-level safe coordination control for efficient and safe navigation of robot swarms, and high-level decision-making mechanisms (such as opinion dynamics) for task coordination and behavioral adaptation. The project will leverage both traditional model-based and modern learning-based control techniques to balance reliability, performance, computational efficiency, and adaptability under uncertainty. Affiliated with the Center for Robotic Autonomy in Demanding and Long-Lasting Environments (CRADLE), the candidate will collaborate with teams working on system architectures and demonstrators, and will refine and verify the proposed framework using formal verification methods. The research will combine rigorous mathematical analysis—drawing on control theory, nonlinear dynamical systems, networked multi-agent systems, and formal methods—with practical validation on robotic platforms such as drones and ground vehicles. The outcomes are expected to contribute to leading conferences and journals in control and robotics. The ideal candidate will have a strong background in control theory and practical robotics experience, with enthusiasm for both theoretical development and hands-on implementation. Applicants should hold at least an Upper Second-Class Honours degree (2:1) or a Master’s degree in Control Engineering, Robotics, Applied Mathematics, or a related quantitative discipline. Research experience in control theory or robotics and hands-on experience with ROS are highly desirable. Funding is available for both home and international applicants. Home applicants (including EU with settled status) are eligible for a studentship covering tuition and a tax-free stipend at the UKRI rate (£20,780 for 2025/26, with annual increases). International applicants may be nominated for faculty-funded scholarships, including the President’s Doctoral, Dean’s Doctoral, CSC/UoM, and Africa Futures Scholarships, all of which cover tuition and stipend. Early application is encouraged as the advert may close before the stated deadline. Applicants are strongly advised to contact Dr. Zhiqi Tang ([email protected]) before applying to discuss their academic background and motivation. Applications must be submitted online, specifying the project title, supervisor, funding status, previous study details, and referee contact information. Required documents include transcripts, CV, supporting statement, and English language certificate if applicable. The University of Manchester is committed to equality, diversity, and inclusion, and encourages applications from all backgrounds, including those returning from career breaks or seeking flexible study arrangements.