Research Associate/Senior Research Associate in Medical Statistics/Data Science
MRC Integrative Epidemiology Unit
Job number ACAD104556
Division/School Bristol Medical School
Contract type Open Ended
Working pattern Full time
Salary Grade I: £33,797 - £38,017 per annum Grade J: £38,017 to £42,792 per annum
Closing date for applications 21-Jun-2020
We have an exciting opportunity for a talented postdoctoral researcher with strong statistical or statistical genetics skills to join the MRC Integrative Epidemiology unit (MRC IEU) at the University of Bristol to work on developing and testing methods to examine causal risk factors for disease progression using large genetic datasets.
The post will build on our recent work developing methods to overcome sources of bias in case-only studies. You will take a lead role in designing simulation studies to test these methods, and work with others to extend, develop and test new methods. You will design statistical packages to implement these methods and write documentation to support users. You will be supported by the methodologists working within the MRC IEU, and collaborations nationally and internationally.
You will have the opportunity to work with others (in the MRC IEU and further afield) to apply the newly developed methods to questions of clinical interest. Within the MRC IEU we have access to several genetic datasets of disease progression phenotypes, and there will be the opportunity to spend time on a research visit to collaborators. This post provides the unique opportunity to test and apply novel methodologies for genome-wide association and Mendelian randomization in the context of disease progression phenotypes.
You will have, or soon be awarded, a PhD in medical statistics, statistical genetics or epidemiology or a related subject, and will have peer-reviewed research outputs either published or in press. You will have skills in statistical programming languages (such as R) and experience in developing or testing new methods. You will have expertise in applied statistical analysis and working with large-scale genetic datasets and will preferably have an understanding of genome-wide association and Mendelian randomization methods.
For informal queries please contact
Professor Kate Tilling (firstname.lastname@example.org)
Prof George Davey Smith (email@example.com)
We welcome applications from all members of our community and are particularly encouraging those from diverse groups, such as members of the LGBT+ and BAME communities, to join us.