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simulation results with experimental data. This project will integrate advanced AI techniques, including machine learning for parameter optimisation (e.g., Bayesian optimisation, reinforcement learning), AI
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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust
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differential equation models of bacterial persistence. A particular challenge, both for simulation and for machine learning, lies in the high dimensionality of these equations, which causes grid-based numerical
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increasingly rely on data-driven models to extract, represent, and interpret information from complex and evolving environments. Traditional machine learning approaches, as well as many classical signal
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machine learning approaches Collaborating with experimental and clinical research partners Support and preparation of scientific reports and journal articles
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, methods, models, and algorithms that integrate general and domain-specific knowledge with data, laying the foundations of next generation machine learning. We do this by combining the mathematical and
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. Desirable Criteria Experience implementing machine learning or deep learning models (e.g., neural networks, probabilistic learning methods). Knowledge of state estimation techniques, such as Kalman filters
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Develop machine learning methods and tools with a specific focus on: Data-Centric AI: Including data attribution, data curation, and privacy preservation for large foundation models (e.g., LLMs and VLMs
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on correlation-based machine learning. When an agricultural system fails due to compounding climate extremes - like a simultaneous heatwave, drought, and ozone pollution spike - standard models can forecast the
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declaration of non-extension. With appropriate work progress, an extension to a total maximum of 4 years is possible. About the team Join the Responsible Machine Learning (ML) Group at the Faculty