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significant advancements with the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques. This PhD research aims to explore the development and application of Process Induced Neural
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capable of predicting boiling and CHF in PWR-relevant conditions. Combining improved physical modelling with the potential of machine learning and data assimilation techniques, you will specifically target
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academic departments regarding concerns about student mental wellbeing via Mental Health Guidance and Liaison Desk. Maintain accurate and timely records on a computer system, working in accordance with
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intelligent sensing, followed by detection of the important events.In the light of autonomous decision making, the project aims at developing machine learning algorithms for knowledge extraction from data
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Machine Learning Methods for Enhancing Autonomy of Unmanned Aerial Vehicles in Wildfire Detection and Localisation
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Artificial intelligence and machine learning methods for model discovery in the social sciences
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Modelling of machining induced damage in Additively Manufactured aerospace parts School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof Hassan Ghadbeigi, Dr K
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research, you will also harness the potential of machine learning by developing a data-driven boiling model derived from its physically-based counterpart, enabling faster and more stable CFD calculations
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of this work is to develop an optimisation procedure aimed towards the automatic generation of chemical reaction networks for fuel thermal degradation. This can be done by adopting a number of machine learning
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between the brain signals of different subjects. The aim of this project is developing new adaptive and machine learning algorithms to successfully decode brain signals across subjects. The prospective