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synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project
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both sites. The project sits at the interface of cell line engineering, protein science and machine learning and you will receive advanced training in these areas while developing methods to accelerate
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This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM
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implement more effective interventions based on up-to-date predictions. The ideal candidate will have foundational knowledge of machine learning and strong self-motivation. You will be supervised by Dr
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Learning heat: Physics-Informed Fourier Neural Operators for High-Fidelity Thermal NDE Modern non-destructive evaluation (NDE) increasingly relies on AI models that can reason with physics, scale
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contribute significantly to these growing fields. This PhD position is ideal for candidates interested in the following areas of machine learning: Geometric learning: exploiting the structure of data (e.g
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, approximately 2000 students are currently enrolled in the College’s taught and research post-graduate courses, including an extensive range of online distance learning diplomas and degrees. They are trained by
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School of Medicine Closing date: 2 January 2026 Start date: 1 February 2026 Defining mechanisms underlying post-translational modification of airway mucus and the impact on mucociliary function
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-agent reinforcement learning (MARL) framework for cyber-physical networked fault-tolerant control of renewable energy-fed smart grids under adversarial conditions [6]-[9]. Multiple autonomous agents will
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for computer, lab, and fieldwork costs necessary for you to conduct your research. There is also a conference budget of £2,000 and individual Training Budget of £1,000 for specialist training Project Aims and