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Research Associate - Safety of AI in Healthcare

Computer Science
Based at
University of York - Heslington Campus
Hours of work
Contract status
Fixed term
32,817 - 40,322 a year
Interview Date
Posted Date
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Job Reference

Role Description


We are seeking a highly-motivated researcher to work as part of the Assuring Autonomy International Programme (AAIP) ( The AAIP is an exciting £12 million initiative funded by Lloyd’s Register Foundation and the University of York to spearhead research in the safety of robotics and autonomous systems (RAS).


The role is concerned with research, as part of the AI Clinician project, into the safety assurance of an AI-based Decision Support System (DSS) for sepsis treatment in the intensive care unit. This encompasses many challenging areas of research including machine learning safety, hazard and risk analysis of complex health interventions and safety cases.

In this role you will engage with clinical and industrial partners particularly colleagues at Imperial College London ( will be part of a vibrant and growing team, and will collaborate with the other research staff in the programme, our experienced programme fellows, and our partner organisations to conduct research. In addition, you will be expected to:

  • validate and analyse the research outputs and results
  • write up research results and disseminate results through publications, seminar and conference presentations and public engagement and outreach activities
  • ensure that the work of the programme has a substantial real-world impact
  • assist in the identification and development of potential areas of research and the development of proposals for independent or collaborative research projects
  • assist with undergraduate teaching in own area of expertise.    

Skills, Experience & Qualification needed

  • First degree in STEM or related discipline, and Doctoral level research experience in machine learning and safety
  • PhD in STEM (science, technology, engineering, and mathematics) or equivalent experience
  • Knowledge in machine learning to engage in high quality research
  • Knowledge of a range of health data analysis techniques and methodologies
  • Knowledge in safety assurance, including safety cases and hazard/risk analysis
  • Highly developed communication skills to engage effectively with a wide-ranging audience, both orally and in writing, using a range of media
  • Ability to identify sources of funding and contribute to the process of securing funds, with collaborators if required
  • Experience of working on safety assurance of systems in industry or academia
  • Experience of working on machine learning systems in safety-critical domains
  • Experience of working with large health datasets

Interview date:  10 September 2020

For informal enquiries: please contact Dr Ibrahim Habli (

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