Statistical Programming Training Fellowship
NDPH, Old Road Campus, Headington, Oxford
Grade 6: Salary in the range £28,660 - £31,302 p.a. (pro rata for part-time)
The Nuffield Department of Population Health (NDPH) contains world-renowned population health research groups and provides an excellent environment for multi-disciplinary research and teaching.
The Statistical Programming Training Fellowship scheme within NDPH represents an exciting opportunity to support post-Masters statisticians in a period of early career training. The aim of the scheme is to give training in applied statistical programming, analytic and transferable skills, in preparation for the broader requirements of a career as a statistical scientist.
The very large-scale clinical and epidemiological data that are increasing available provide the opportunity for many exciting and extensive investigations but require high-quality well-structured data manipulation and statistical analysis programming to yield their full potential. This specialist training fellowship is offered to a candidate with a Masters in Medical Statistics or a closely related field involving substantial training in statistics. Statistical programming training will be provided in SAS, R and genetics software on large datasets (including genetic and processed imaging data) to attain milestones that will facilitate the fellow's future 'Big Data' research.
Eligible candidates for this opportunity will demonstrate excellent potential (on a par with competitive admission to a DPhil) to proceed to a research career involving advanced statistical programming and analyses of large-scale datasets.
The post is fixed-term for 1 year from October 2019 to September 2020 and full-time (although part-time considered).
The closing date for applications is 12.00 noon on 19 July 2019.
Further details about the post and how to apply are available on the NDPH website:
Informal enquiries should be addressed to either Dr Derrick Bennett ([log in to unmask]<mailto:[log in to unmask]>) or Professor Sarah Parish ([log in to unmask]<mailto:[log in to unmask]>)
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