[Apologies for multiple posting]

UNIVERSITY COLLEGE LONDON GREAT ORMOND STREET INSTITUTE OF CHILD HEALTH -- RESEARCH FELLOW (https://www.jobs.ac.uk/job/BNE372/research-fellow)

Closing date on 11 November

This is an exciting opportunity for a methodological statistician to develop their career in causal inference applied to observational data. S/he will also have opportunity to gain skills and experience in analysing "big data" for health research.

The post-holder will be a member of a team working to develop guidelines for the "emulate the target trial" framework developed by Hernán & Robins (2016) for comparative effectiveness research when applied to routinely collected health data. The focus will be on addressing the potential biases arising from the quality of these data and on learning from the substantive analyses of three exemplars arising from linked UK health records.

The appointee will be expected to:

  *   Work collaboratively with methodologists and epidemiologists
  *   Contribute to the development of the roadmap for the implementation of the ETT approaches, including relevant computer code (in R and Stata);
  *   Contribute to the analyses of exemplars;
  *   Support the dissemination of the research output, by presenting work at conferences, contributing to teaching for dedicated short course, and maintaining the project's webpage.

The successful candidate will have:

  *   A PhD in Medical Statistics/Epidemiology/Bioinformatics or a closely related subject;
  *   Strong working knowledge of research methods in statistics and epidemiology;
  *   Interest in causal inference methods;
  *   Research experience of analysing observational data using Stata and/or R;
  *   Work effectively and collaboratively.


Location:

London

Salary:

£35,328 to £39,353 per annum, inclusive of London Allowance (Grade 7)

Hours:

Full Time

Contract Type:

Fixed-Term/Contract

Closes:

12th November 2018

Job Ref:

1753909



For further information please contact Prof Bianca De Stavola ([log in to unmask]<mailto:[log in to unmask]>)

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