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Field
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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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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
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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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of award3 years EligibilityUK, EU, Rest of world Entry requirements Applicants should have an equivalent of first or second class UK honours degree or equivalent in a related discipline, science (chemistry
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local gas/liquid phase conditions. Whilst direct simulations of breakup are possible, computational cost is high, restricting applications to small sections of geometry and for modest run times
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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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spaces and habits for them. This is a highly interdisciplinary project that combines computational modelling and behavioural science. The first part will be based on the use of state-of-the-art
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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