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UCL Department: Great Ormond Street Institute of Child Health Specific unit: Population, Policy and Practice / Administrative Data Research Centre for England Salary (inclusive of London allowance): £54,240 - £58,978 per annum Closing date: 30 September 2017 We seek an experienced, quantitative senior lecturer with a strong track record in research using administrative data. The post is based at the Administrative Data Research Centre for England (ADRC-E<https://adrn.ac.uk/centres/england>) which is part of the Population Policy and Practice Programme (PPP<http://www.ucl.ac.uk/ich/research/population-policy-practice>) at the UCL Great Ormond Street Institute of Child Health (UCLGOSICH). The post-holder will be located on two sites at PPP UCLGOSICH and at the ADRC-E at the UCL Institute of Health Informatics (222 Euston Rd). After satisfactory completion of the three-year probationary period the appointment will be open ended. The senior lecturer will have excellent skills in quantitative analyse using large, longitudinal administrative data. Expertise in related data resources, such as cohorts, surveys or trials linked to administrative data would be an advantage. The senior lecturer will have detailed knowledge of administrative data from at least one service sector (e.g.; health, finance, education). The aim of the post is to advance and widen the use of quantitative methods for research using linked administrative data, to build capacity in this area through attracting grants and contributing to teaching, and to contribute to developing the scientific strategy within the PPP UCLGOSICH. The senior lecturer will be expected to develop and lead an area of research which could focus on methodology or be a programme of research applied to policy or organisation of services. Relevance to children or families is desirable. The research should address the aims of the ADRC-E, which are to widen the use of administrative data for research. The post would be suitable for a highly skilled quantitative senior lecturer who wants to address the challenges of using linkage of large administrative datasets to inform policy, service development, and/or methodological advances. For more information and to apply, please visit the UCL jobs website<https://atsv7.wcn.co.uk/search_engine/jobs.cgi?SID=amNvZGU9MTY2NjgxNSZ2dF90ZW1wbGF0ZT05NjUmb3duZXI9NTA0MTE3OCZvd25lcnR5cGU9ZmFpciZicmFuZF9pZD0wJmpvYl9yZWZfY29kZT0xNjY2ODE1JnBvc3RpbmdfY29kZT0yMjQ> (ref: 1666815) You may leave the list at any time by sending the command SIGNOFF allstat to [log in to unmask], leaving the subject line blank.