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Field
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. The Centre for Excellence in Coatings & Surface Eng at Nottingham www.nottingham.ac.uk/coatings is an international reference for all Thermal Barrier Coating activities. This PhD programme, in partnership
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equipment not available elsewhere. This project is aligned with the “Dialling up Performance for on Demand Manufacturing” Programme Grant, which will place the student within an active and supportive team
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, requiring large computational effort to assess and study system stability. This is becoming even more challenging under increasing complexity requiring detailed dynamical models and with new dynamic phenomena
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the field of Computational Morphodynamics in plants. The work will be within the ERC-funded project RESYDE (https://resydeproject.org ) with the aim of building a virtual flower using multi-level data and
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work on the INSTINCT-MB programme, which brings together teams based at Newcastle University, The Institute of Cancer Research and University College London. The programme will generate a wide range of
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but not essential. A strong background in materials science and/or modelling is essential. Experience in machine learning, computer vision, and computer programming is desirable. In addition
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(or equivalent) in a biomedical science. Experience in neuroscience and/or immunology is desirable. Project key words Retinal imaging, data-analytics, computer vision, big data Funding The studentship, funded by
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disturbances. This programme of doctoral work will investigate the prevalence of iron deficiency/ iron deficiency anaemia in high performing athletes and assess the effectiveness of a novel oral iron supplement
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tailored metallurgy-chemistry of porous structures, it still lacks surface finishing quality. This project will investigate the science behind surface finishing and the control of metal porosity engineered
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sources, information (mis)alignment, domain shift, missing data modality, data privacy, data and computing cost. It will focus on targeted scientific problems to test the solutions, aiming at a lowest