Machine Learning Scientist in Urban Health

University of Oxford - Nuffield Department of Obstetrics and Gynaecology

Nuffield Department of Obstetrics and Gynaecology, The George Institute for Global Health, Le Gros Clark Building, South Parks Road, Oxford

Do you have expertise in Machine Learning? Could you use this experience to help us create game-changing solutions for healthcare problems?

The George Institute for Global Health, part of the Nuffield Department of Obstetrics and Gynaecology, is looking for two Machine Learning Scientists to join the team as part of the Oxford Martin School’s prestigious program on Deep Medicine. The program is focused on solving high-impact healthcare problems, with the application of modern machine learning algorithms to large multi-modal (e.g. genetics, imaging, medical records) biomedical datasets.

These positions are part of the RCUK funded ‘PEAK Urban’ programme and are available from 1 April 2018 for 3 years. You will join an international team of postdoctoral scholars within the PEAK Urban project, a 4-year, international, multidisciplinary GCRF programme involving Oxford researchers at Anthropology (COMPAS), the Mathematical Institute, Geography (Transport Studies Unit) and Medicine (George Institute/Deep Medicine), along with universities in China (Peking University), South Africa (University of Cape Town), India (Indian Institute for Human Settlements) and Colombia (EAFIT University). See: for more details.

Your responsibilities will include employing the existing (and developing new) research methodologies that can find patterns in large-scale data, innovating and providing solutions for public health, undertaking project management and monitoring functions, including overseeing the organisation of stakeholder meetings and collaborating with a network of international academics, and linking with policy-makers based in both governmental and non-governmental organisations.

You will hold a doctorate (or near completion) in mathematics, engineering or a related discipline (including medical or social sciences with a significant data-science component). You will have scientific expertise and applied experience in Machine Learning, in depth understanding of common Machine Learning algorithms and a track record in advanced topics of Machine Learning or related methodologies. You will also have advanced programming skills in Python and/or R and practical experience in preparing data for Machine Learning.  Experience of working with health data, integration of Machine Learning algorithms with big-data platforms (e.g. Spark) and high-performance computing ecosystems (e.g., CUDA) as well as Programming in C++ and Java would also be an advantage.

This position is full-time and fixed-term for 3 years. This post is available from 1 April 2018 or as soon as possible thereafter. Applications for flexible working arrangements are welcomed and will be considered in line with business needs.

You will be required to upload a CV and supporting statement as part of your online application.

The closing date for applications is 12.00 noon on Monday 8 January 2018. Interviews are expected to take place on Wednesday 17 January 2018.

Please note that the University of Oxford's retirement policy has changed. With effect from 1 October 2017, all employees at Grade 8 and above have a retirement age of the 30 September before the 69th birthday. All employees at Grades 1-7 do not have a set retirement age. Further details are available here:

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South East England