Postdoctoral Research Assistant in Model Selection
Posted: 26th June 2015
Fixed-term for up to 3 years
Grade 7: £30,434 – £37,394 p.a.
We are looking for a highly motivated researcher with a documented track record of successful research projects and a desire to do interdisciplinary research. This postdoctoral position will focus on Bayesian approaches to uncertainty in electrophysiology models, the selection of these models, and optimal experimental design for these tasks. The research will be carried out under the supervision of Dr. Gary Mirams as part of a Wellcome Trust and Royal Society funded fellowship project entitled “Improving assessment of drug-induced cardiac risk with mathematical electrophysiology models”, and Prof. David Gavaghan under an EPSRC Cross Disciplinary Interfaces Programme (www.2020science.net).
You will have a doctorate or equivalent degree in a relevant discipline, have evidence of independent research, and have experience in the area of statistical modelling of complex systems. Additionally, you will have a keen interest in the application of mathematical and statistical models to biological data and an eagerness to learn about the modelling approaches used in electrophysiology. Excellent programming and statistical skills are essential, and you will be expected to produce leading scientific research and publish results in scientific journals.
The post, which is a full-time appointment, is funded by the Wellcome Trust, and the Royal Society, is available for up to 3 years, and has a salary on the University grade 07S scale (currently £30,434 to £37,394 p.a.). This includes membership of the University Superannuation Scheme (USS) and has an annual leave entitlement of 38 days per year (inclusive of all public holidays and university closed periods). Requests to work on a part time or flexible basis will be considered.
The closing date for applications is 12 noon on 29 July 2015.
For further details and to apply please visit: https://www.recruit.ox.ac.uk/pls/hrisliverecruit/erq_jobspec_version_4.jobspec?p_id=118772