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Field
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field. This approach is related to data assimilation, allowing for better prediction, control, and optimisation of turbulent systems in engineering, energy, and environmental applications
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into model-predictive control (MPC) or reinforcement learning (RL) frameworks to compute optimal exoskeleton assistance in real time. Validating the developed methods in human experiments using motion capture
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: Textual Prediction of Survival (LLM classification & Attention Modelling) This project develops a model to predict patient survival by analyzing heterogeneous clinical documents. Unlike traditional methods
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Gaussian process regression to represent unknown dynamics for model predictive control. Despite the practical success, there are still many theoretical open questions regarding scalability, uncertainty
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of existing studies to promote the use of risk-informed decision frameworks, prediction models, AI applied to planetary protection. Tasks include: Support the creation of probabilistic models for planetary
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parameters using experimental muscle and neural recordings Explore motor control policies that replicate observed behaviours Test simulation predictions against muscle ablation experiments Investigate how
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additional controlled evaluations using both simulated data and real ephemerides, including Low Earth Orbit satellites. The grant will prioritise: quantitative validation of prediction accuracy
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identification, i.e. learning of models from measured data, and iii) real-time control, e.g. using the model predictive approach. We are working on several projects with industrial partners across the energy
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mechanisms of adaptive and acquired drug resistance, exploring network-level control and feedback in cell signaling systems, identifying novel drug targets and therapeutic strategies, and developing predictive
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geometries and process-induced defects demand new inspection approaches. The project combines modelling, sensor fabrication, experiment, and data analysis. You will work with a team of experts to develop