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researchers eager to contribute to this emerging scientific frontier. About the project The role of the PhD candidate will be to develop efficient methods for Hybrid Learning-Control Methods for Autonomous
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16th October 2026 Languages English English English The Department of Engineering Cybernetics has a vacancy for a PhD Candidate in Hybrid Learning-Control Methods for Autonomous Underwater
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PhD Scholarship This project will develop the next generation of intelligent robotic systems capable of autonomously manipulating small, soft and deformable objects in confined environments
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inexpensive microfluidic techniques. While the mechanism of these drops’ motion is well-known, controlling their trajectory is often challenging. There exist experimental proofs-of-concept that utilize imposed
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systems, from sustainable energy grids to autonomous mobility. In this ERC-funded PhD project, you develop cutting-edge game-theoretic control and optimization methods. Job description Modern society
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Modern autonomous ground vehicles (AGVs/UGVs) in defence operations require sophisticated AI/ML-based control systems for perception, decision-making, and adaptive responses in complex, unstructured
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, visiting researchers, master's students, etc.) Research Context Recent advances in mobile robotics have been driven by remarkable progress in perception, deep learning, and control. However, current robotic
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robotics. Specific application areas of focus are long-term autonomous missions in large and uncertain environments, semantic mission planning with foundation models, agentic task decomposition and event
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Information Areas for Smart Water Systems (DIAS). These will operate as data-informed virtual subnetworks that complement existing District Metered Areas and support coordinated, semi-autonomous decision-making
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Engineering at the University of Sheffield. A leading centre for machine learning, robotics, and autonomous systems. The student will join a research group working at the interface of machine learning, control