The Oxford Martin programme on Deep Medicine is using some of the largest and most complex biomedical datasets that have ever been collected to generate insights into complex diseases and treatments. Through an interdisciplinary approach, established and novel techniques in data mining, machine learning and deep learning will be applied to complex biomedical datasets. The advent of ‘Big Data’ can offer unprecedented opportunities for extending our knowledge and understanding of complex disease patterns and risk, but harnessing its full potential will require new and innovative methods of analysis. Our groups have world-leading expertise in medical imaging, image understanding, and machine learning. The postholder will be jointly a member of the FMRIB Analysis research group (which is leading the brain imaging component of UK Biobank, the largest neuroimaging study ever, with 100,000 subjects to be scanned and then studied for disease outcomes), and the Visual Geometry group in Engineering (which includes world-leading expertise and research on the development and application of Deep Learning). We are looking for a researcher who can contribute expertise in applying Deep Learning to medical imaging data from both the brain imaging and the cardiac imaging data in UK Biobank. Both kinds of medical imaging data are extremely rich and complex, and are already available from the first 20,000 subjects taking part in UK Biobank. Relating these raw imaging data to factors like biological and life measures (blood pressure, smoking, etc) and disease outcomes is a huge computational challenge and we hope to make great progress through the use of Deep Learning methods. You will hold (or be nearing completion of) a PhD in a relevant quantitative domain, such as engineering, physics, mathematics or statistics, and have expertise in Deep Learning or suitably related machine learning methods. For further details about this position please contact: Professor Steve Smith ([log in to unmask]) and Professor Andrea Vedaldi (v[log in to unmask]). The post is full-time for a fixed-term (2 year) contract in the first instance. Only applications received before 12.00 midday on Monday 16 March 2018 will be considered. Interviews will be held as soon as possible thereafter. |