Research Fellow in Statistics - University of Southampton, UK
Salary: £29,799 - £35.550
Closing data: Saturday 30 June 2018
Fixed term until 30/04/2021
Applications are invited for a researcher in Statistics within the Southampton Statistical Sciences Research Institute and Mathematical Sciences at the University of Southampton.
This position is part of an EPSRC-funded multidisciplinary collaborative project between Statistics at the University of Southampton, Chemical Engineering at the University of Cambridge and Chemistry at the University of Glasgow. The project will develop methodology for robust discovery of complex chemical products using automated techniques.
The Southampton arm of the project is led by Professor David Woods. The successful applicant will also collaborate with the other grant investigators and supporting research staff and PhD students. The opportunity may arise to co-supervise a PhD student whose project is in a related area.
The main focus of the project will be the development, implementation and application of novel methods for Bayesian optimal design of experiments for complex nonlinear models. To effectively address the substantive applications from chemistry and chemical engineering, the developed methodology will need to address multi-stage and multi-level sequential experimentation, using appropriate computational techniques to facilitate the efficient search for optimal designs. The research fellow will be a proactive researcher who has, or is about to obtain, a PhD in Statistics or with equivalent research experience. You will have good communication skills and be able to interact effectively with collaborators in Chemistry, Chemical Engineering, and other scientific disciplines. A background in Design of Experiments and/or Bayesian modelling and computation is essential, and experience of Bayesian design of experiments is desirable (but not necessary).
Informal enquiries are encouraged and may be made to Professor David Woods, telephone +44 (0)23 8059 5117, email: This email address is being protected from spambots. You need JavaScript enabled to view it..
Online application and further details are available at https://jobs.soton.ac.uk/Vacancy.aspx?ref=1013818PJ. Reference 1013818PJ should be quoted on all correspondence.
