This PhD opportunity 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 resilient and adaptable 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 robots to make collective decisions and adapt to changing conditions. The project aims to create a hierarchical control framework that tightly integrates cognitive decision-making with physical systems control. The research will address 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 in response to internal and external changes. Both model-based and learning-based control techniques will be explored to balance reliability, performance, computational efficiency, and adaptability under uncertainty. Affiliation with CRADLE (Center for Robotic Autonomy in Demanding and Long-Lasting Environments) provides access to interdisciplinary expertise and collaborative research. The candidate will work closely with CRADLE’s Work Package 2 (Architectures) and Work Package 5 (Demonstrators), refining and verifying the proposed framework using formal verification techniques. Theoretical innovation will be grounded in rigorous mathematical analysis, leveraging control theory, nonlinear dynamical systems, networked multi-agent systems, and formal methods. Practical validation will involve case studies on robotic platforms such as drones and ground vehicles, demonstrating real-world applicability. The supervisory team includes Dr. Zhiqi Tang (Department of Electronic and Electrical Engineering), Professor Simon Watson (Department of Electronic and Electrical Engineering), and Professor Michael Fisher (Department of Computer Science). The ideal candidate will have a strong background in control theory and practical robotics experience, with enthusiasm for both theoretical and hands-on research. Experience with ROS and prior research in control or robotics is advantageous. Funding is available for a 3.5-year PhD studentship, open to Home (UK) and EU applicants with settled status, offering an annual tax-free stipend at the UKRI rate (£20,780 for 2025/26, subject to annual uplift) and full tuition coverage. A second position may be available for home and overseas applicants via faculty funding. The application deadline is February 28, 2026, but early application is recommended as the advert may be removed once filled. 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 hands-on experience with robotic platforms is a plus. English language certification is required for non-native speakers. To apply, candidates should contact Dr. Zhiqi Tang ([email protected]) before submitting their application to discuss their background and motivation. Applications must be submitted online via the university’s portal, specifying the project title, supervisor, funding status, previous study details, and referee contacts. Required documents include transcripts, CV, supporting statement, and English language certificate if applicable. The university values equality, diversity, and inclusion, and encourages applicants from all backgrounds, including those returning from career breaks or seeking flexible study arrangements.

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.