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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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candidate will design algorithms that identify suspicious model updates, reduce the influence of compromised UAVs, and preserve useful learning from honest UAVs operating with different data and unreliable
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control platforms, advanced microcontrollers, distributed control algorithms, and artificial intelligence techniques, including neural networks and evolutionary optimisation methods, to enable the efficient
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to evaluate and advance machine learning algorithms for one of the following application areas: “Characterising forests variations in relation to distance from pre-Columbian earthworks in the Amazon forest
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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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algorithms. Vision Language Model (VLM) experience is desirable. Qualifications A high-grade undergraduate degree (first class or upper second) in computer science or MSc in related field. Skills Knowledge
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of posterior curvature. The project will establish optimised OCT acquisition and de-warping methods, build robust segmentation and curvature-analysis algorithms, and validate clinically meaningful shape metrics
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and algorithmic framework to assess and improve the resilience of FL-enabled autonomous systems under such heterogeneity, explicitly incorporating the human in the loop. The project will draw
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the nonlinear dynamics of these networks using theory and numerical simulations. - Design and perform table-top robotic experiments that implement your learning algorithms in unpredictable environments. Who