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We are looking for a Postdoctoral Researcher in Diffusion MRI Methods, the post will be based within the Oxford Centre for Functional MRI of the Brain (FMRIB), reporting to Professor Karla Miller
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close to the completion of a relevant PhD/DPhil in statistical genetics or related field. A proven track record of method development in statistical genetic and the ability to manage your own academic
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Research Council (MRC) and the National Institute for Health and Care Research (NIHR), the project aims to advance artificial intelligence (AI) methods that improve the reliability of clinical prediction
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The Kelly lab welcomes individuals with diverse career backgrounds – PhD-level scientists in any discipline with expertise in data and programming, or software engineers outside of academia looking to change
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particular emphasis on kinetic Monte Carlo approaches, and in software development, including version control (Git or similar). Experience in ab initio methods and specifically calculations of excited
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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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of new methods relevant for drug discovery analysis pipelines, trial design and operational efficiency. Other responsibilities will include helping with supervision of PhD students working on projects as
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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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understanding of dark energy. Projects may span a broad range of topics, including improving Type Ia supernova modelling and standardization, developing and applying advanced data analysis and statistical methods
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of comparative genomics and molecular evolution analyses to identify these targets. The project will make use of software developed in the Kelly group as well as other widely used software for biological sequence