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your suitability with evidence of the following: Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics. Knowledgeable in machine learning techniques (had
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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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Characterisation" "Data Science and Machine Learning in Materials" "Plastics Recycling and Circular Economy" Research theme: "Materials Characterisation" "Data Science and Machine Learning in Materials" "Plastics
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Engineering, and Engineering Management. Students with interests in computational mechanics, optimization design, bioinspired design, sustainability management, machine learning, AI, uncertainty quantification
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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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motivated PhD student to join our interdisciplinary team to help address critical challenges in high-speed electrical machine design for electrified transportation and power generation. Together, we will make
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This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites Research Groups at the Faculty of Engineering, which conduct cutting-edge research
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considered. Qualifications/Skills PhD degree in a programme relevant to human-computer interaction and/or critical computing, ideally in Computer Science, Industrial Engineering, Interaction Design, or a
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fully funded PhD research studentship in Physics-Informed Machine Learning for Cardiovascular Medicine. This opportunity is open to UK (Home) candidates only. Project Overview Arrhythmias are disorders
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems