Please see the advert
below:
Statistical
modelling
of carbon capture and transport.
Supervised
by Dr Richard Graham, Dr Richard Wilkinson and Dr Simon
Preston
Carbon capture and storage is a crucial technology in the international efforts to mitigate climate change. Essential to this program is accurate modelling of the physical properties of carbon dioxide mixtures. Currently models are parametric, are fitted in an ad-hoc way and have no uncertainty quantification. This project will develop Monte-Carlo approaches for Bayesian parameter estimation, and will use modern machine-learning methods to produce non-parametric models. Quantification of uncertainty will also be an essential element.
The project will develop a flexible methodology for carbon dioxide modelling, tailored to particular applications. We will have access to novel, high quality measurements from experiments ongoing at the University of Nottingham. This project addresses an urgent industrial need and we have numerous active collaborations with industrial partners who are eager to use new models for the design, operation and optimisation of the UK’s carbon capture infrastructure. The project has a confirmed studentship from the School of Mathematical Sciences.
We require an enthusiastic graduate with a 1st class degree in Mathematics (in exceptional circumstances a 2(i) class degree can be considered), preferably of the MMath/MSc level. A candidate with a solid background in Probability and Statistics and/or good programming skills will have an advantage.
The studentship is available from September/October 2013 and provides an annual stipend at the standard EPSRC rate (currently £13,590 per annum) and full payment of Home/EU Tuition Fees. Students must meet the EPSRC eligibility criteria. The studentship period will depend on the training needs of the successful applicant.
Informal enquiries should be addressed to Dr Richard Graham, email: [log in to unmask].
To apply, please access: https://my.nottingham.ac.uk/pgapps/welcome/. This studentship is open until filled. Early application is strongly encouraged.