Marie Skłodowska-Curie Innovative Training Network Early-Stage Researcher (ESR) Fellowship in AI...

Updated: 4 months ago
Location: Oxford, ENGLAND
Job Type: FullTime
Deadline: 23 Jun 2021

A full-time Fellowship position is available for a period of 3 years, tenable at Oxford University, on a project entitled Machine Learning with Healthcare Data. The successful candidate will be registered for PhD training while engaged by the University on a Marie Skłodowska-Curie Innovative Training Network, and will be expected to conduct research on developing machine learning methods to improve the understanding of monitoring complex systems, across a number of high-profile collaborations. A key aspect of the Oxford work will be developing these models within healthcare applications, with tools for tackling COVID-19 being a priority. Candidates must possess a good degree in a relevant subject and should have a good knowledge of machine learning algorithms (including deep learning), as well as proven competence in programming methods in Python and TensorFlow. This post is part of a collaboration with the EU Innovative Training Network “MOIRA” and the start date for this position is subject to discussion; the latest possible date is 1 October 2021.

The ITN Fellow will join the Computational Health Informatics (CHI) Laboratory, in the Institute of Biomedical Engineering, in the Department of Engineering Science (Headington, Oxford). The CHI Lab is one of the leading groups for AI in Healthcare, and one of the largest groups in the Department of Engineering Science, with a friendly, close-knit collaborative team focused on delivering novel innovations into healthcare practice.

The Fellowship is offered in conjunction with a PhD position at the University of Oxford and will be subject to the Fellow satisfying the University’s admissions requirements for PhD study. You should therefore be prepared to undertake, or are already undertaking a doctoral degree in machine learning for healthcare, to be associated with the CHI Lab. You should also have experience of working in a highly interdisciplinary team, with a good publication record in the scientific literature.

The EC funding for this position provides for a remuneration starting from £51,714 (€62,056) per annum. The actual salary will depend on employer deductions, personal circumstances and the exchange rate applicable to the fellowship. This amount includes an annual living allowance and a mobility allowance (to cover the expenses associated with working in a different country).

Under the terms of the EC funding, which aims to promote mobility within the research community, to be eligible for the post you:
- Must not have been resident in the UK for more than a total of 12 months in the past three years.
- Must not already have obtained a doctorate or had more than 4 years full time research experience

Informal enquiries may be addressed to Prof. David Clifton, Professor of Clinical Machine Learning, within the Department of Engineering Science (email: davidc@robots.ox.ac.uk ).

Only online applications received before midday on 23 June 2021 can be considered. You will be required to upload a covering letter/supporting statement, including a brief statement of research interests (describing how past experience and future plans fit with the advertised position), CV and the details of two referees as part of your online application.

To apply for this role and for further details including the job description and selection criteria, please visit the following link: https://my.corehr.com/pls/uoxrecruit/erq_jobspec_version_4.display_form?...

Candidates will also need to make a second online application for the DPhil in Engineering Science. Please visit: https://eng.ox.ac.uk/study/research-studentships/fellowship-in-ai-for-he... . For further details on the University’s DPhil in Engineering Science which gives details about entry requirements, please see: www.ox.ac.uk/admissions/graduate/courses/dphil-engineering-science
The Department holds an Athena Swan Bronze award, highlighting its commitment to promoting women in Science, Engineering and Technology.


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