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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
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experimental techniques, such as machine learning, two dimensional gas chromatography, as well as test equipment for quantification of basic physico-chemical properties of aviation fuels. The PhD programme will
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data-driven machine learning methods to develop scalable and accurate novel computational tools. Further project details can be found at: https://www.sheffield.ac.uk/acse/phd/phd-position-control-theory