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statistical and computational methods designed to use “big data” and to address questions of direct or indirect relevance to common complex diseases and disorders. The appointee will join the group of Professor
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statistical and computational methods designed to use “big data” and to address questions of direct or indirect relevance to common complex diseases and disorders. The appointee will join the group of Professor
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to completion of) a PhD/DPhil in geotechnical engineering, along with experience in numerical methods, including the implementation of soil constitutive models in finite element code. You should have excellent
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imaging data and finite element modelling is a plus. You will have experience in writing publications and be happy working in a collaborative environment. About the School The role will be based
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focuses on improving our understanding of multiple sclerosis disease progression and how treatment can impact progression. This work will focus on the unique Novartis Oxford MS (NO.MS) dataset, the largest
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predictive analysis for Novartis Oxford collaboration for AI in medicine. The collaboration focuses on improving our understanding of multiple sclerosis disease progression and how treatment can impact
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administrative duties and provide guidance in machine learning methods to less experienced members of the research group, participate in the dissemination of research outputs, and carry out collaborative projects
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and method development, is highly beneficial. You will be expected to manage your own academic research and administrative activities. This involves project management, to co-ordinate multiple aspects
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modelling to study and generate a new understanding of MoSS. These advances will have applications across multiple sectors, including pharmaceuticals, agrochemicals, and food industries. There are multiple
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to study and generate a new understanding of MoSS. These advances will have applications across multiple sectors, including pharmaceuticals, agrochemicals, and food industries. There are multiple