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to develop and implement state estimation algorithms, such as Kalman filters and observers, for real-time applications. Proficiency in MATLAB and Simulink for modelling, simulation, and validation using
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persist across participants, and retraining from scratch is often computationally infeasible at scale. In this project, you will develop the next generation of federated machine unlearning algorithms
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update behaviour, validation impact, temporal consistency, and communication reliability. Develop a privacy-aware trust-calibrated aggregation algorithm to down-weight suspicious updates. Evaluate accuracy
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significant reductions in manufacturing-related CO₂ emissions. The ambition is to develop innovative technological solutions that reduce energy losses and minimise environmental impact throughout the product
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algorithms, validated in real-time simulations and experimentally in collaboration with industry. The project: This project looks at the development of electromechanical friction wheel braking (EMB) systems
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with ordinary facial movement. While many methods have been developed to recognise and localise micro-expressions, these results remain difficult to understand. The proposed models will be designed
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, and an interest in translational medical-device research. The work is structured in three phases: protocol development and ex vivo validation; algorithm refinement and an in vivo pilot; and clinical
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) to train shared models without centralising raw data. In practice, however, individual platforms operate under diverse environmental conditions, sensor calibrations, and system configurations
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after deployment, leveraging recent advances in physical reservoir computing, contrastive learning, and biological decision-making paradigms. You will: - Develop and apply decentralized learning
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, speech, gaze, affective dynamics and interaction patterns. This PhD project will develop personalised AI models for computational mental health by simulating the latent cognitive processes underlying