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alongside numerical simulations relying on high-performance computing and reduced order modelling. We aim to gain new insights about the physical coherent structures which are most relevant to viscoelastic
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considering various elements in the process. The integration of computer vision techniques in this project will also enhance the value of this work. The students will not only gain an experience and insight
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of the brain, caused by a virus, an infection, or an autoimmune response. Following encephalitis, children and young people can experience physical/neurological consequences (e.g., headaches, seizures, sleep
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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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biomedical engineering). Flexible start date! What You’ll Need A first-class or upper second-class honours degree (or equivalent) in Engineering, Physics, or Applied Mathematics. Experience in coding and CFD
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. Ideal Candidate: You’re a highly motivated student with a first-class or upper second-class honours degree (or equivalent) in Engineering, Physics, or Applied Mathematics, or a related field. Strong
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with a first class or upper second-class degree in engineering, physics, applied mathematics or a related field. A solid foundation in fluid dynamics and heat transfer, and experience with computer
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Engineering, Computer Science or related disciplines. Experience in autonomous system, manufacturing/robotics and machine vision development will be an advantage. To apply please contact the supervisor, Dr Kun
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-2026 JANUARY 2026 start ONLY For April and October starts please use the relevant application. This form is only to be used by those self-funded applicants seeking a place on a research degree programme
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-2026 April 2026 start ONLY For January and October starts please use the relevant application. This form is only to be used by those self-funded applicants seeking a place on a research degree programme