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Field
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. 4. Advanced Control and Modelling Multi-modular distributed motor-drive architectures and ultra-fast semiconductor devices present significant control challenges. Research may involve FPGA-based
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work will involve: Design and synthesis of injectable hydrogel systems Encapsulation and controlled delivery of probiotics and bacteriophages Engineering and characterization of phage-based antimicrobial
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to science, engineering and society. You will focus on optimization-based, data-driven and partially model-based control methods. You will have access to a strong research network and a broad range of
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We are working on creating a new type of Radio-Frequency Integrated Circuit technology based on optically induced plasmas in silicon to create photo-conductive switches. This combines low-cost
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estimation, and performance prediction. Build computationally efficient models suitable for monitoring, performance prediction, optimization, and control, and evaluate their accuracy, robustness, computational
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. Sequential decision making. MDP/POMDP, dynamic programming, model-based RL, latent state-space (world) models, Monte-Carlo Tree Search. Strong command of at least one, working knowledge of a second: Formal
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. The research will focus on developing hybrid learning–control architectures that integrate model-based control and planning methods with data-driven learning approaches. Potential topics include safe
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support the development of safer flood defence systems and more reliable flood risk assessments. Your responsibilities As a PhD researcher, you will: Develop continuum-based two-phase models to predict
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behaviour for system monitoring, state estimation, and performance prediction. Build computationally efficient models suitable for monitoring, performance prediction, optimization, and control, and evaluate
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of control systems theory to create a new generation of intelligent underwater robotic systems. The research will focus on developing hybrid learning–control architectures that integrate model-based control