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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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(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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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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PhD Studentship: Sleep and Circadian Rhythms in Elite Sport (Co-funded by Brighton & Hove Albion FC)
at £1.2million project on Sleep and Mental Health. Dr Dominique Makowski with expertise in psychophysiology and computational neuropsychology. Dr Sean Figgins with a background and expertise in Sports Science, and
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Solutions, you will apply your methods in detailed simulation studies using Imperial’s high-performance computing facilities, assessing performance in realistic scenarios. You will regularly present your
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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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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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by the Ada Lovelace Centre and the University of Birmingham. This interdisciplinary project is ideal for candidates with a background in physics, materials science, chemistry, or computational science
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the genetic factors influencing changes in brain structures, using brain imaging, computational and statistical methods of network science. Project Aim: The aim of the project is to uncover the complex
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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