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Fellowship in AI for Healthcare

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

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.

You should 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.  

Eligibility

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. This is measured from the date when you obtained the degree which would formally entitle you to embark on a doctorate, either in the country in which the degree was obtained, or in the country in which the research training is provided, irrespective of whether or not a doctorate is envisaged.

Exceptions to these criteria cannot be made.

In addition, candidates will be judged according to how well they meet the criteria detailed in the role profile.

Award Value

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).  

Application Procedure

Informal enquiries are encouraged and should be addressed to to Prof. David Clifton, Professor of Clinical Machine Learning (davidc@robots.ox.ac.uk).

Candidates must submit a graduate application form and are expected to meet the graduate admissions criteria.  Details are available on the course page of the University website.  Please also ensure that you note the English language requirement.

Please quote 21ENGBM_DC in all correspondence and in your graduate application.

Candidates will also need to make a second online application for the Fellowship position by using this link: https://my.corehr.com/pls/uoxrecruit/erq_jobspec_version_4.display_form?p_company=10&p_internal_external=E&p_display_in_irish=N&p_process_type=&p_applicant_no=&p_form_profile_detail=&p_display_apply_ind=Y&p_refresh_search=Y&p_recruitment_id=150631

Application deadline: noon on 23 June 2021

Start date: No later than October 2021