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Research Fellow in Statistics/AI - University of Southampton, UK Salary: £29,799 - £32,548 Closing data: ***Thursday 16 August 2018*** Fixed term for 18 months 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 and Chemistry at the University of Southampton. The project will develop and implement statistical methodology to facilitate crystal structure prediction, in particular robust characterisation of local lattice energy minima using computational models. The overall aim is to aid in the prediction of polymorphism in pharmaceutical chemicals (the ability of a material to exist in more than one crystal structure). The main focus will be the design and modelling of computer experiments and Bayesian optimisation, using the sequential collection of lattice energy model simulations to (a) build computationally inexpensive emulators (e.g. using Gaussian processes) for the energy surface, and (b) provide accurate and precise predictions in the vicinity of identified energy minima. A range of energy simulation models are available, differing in computational cost and accuracy, and the project will design and analyse computational experiments that exploit this hierarchy. The Statistics arm of the project is led by Professor David Woods. The successful applicant will also collaborate with the other grant investigators (Professors Graeme Day and Simon Coles from Chemistry) and a dedicated postdoctoral researcher in Chemistry. You will be a proactive and experienced 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 and other scientific disciplines. A background in Design of Experiments, Bayesian Modelling and Computation or Statistical Machine Learning is essential, and experience of Bayesian optimisation is desirable. Informal enquiries are encouraged and may be made to Professor David Woods, telephone +44 (0)23 8059 5117, email: [log in to unmask] Online application and further details are available at https://jobs.soton.ac.uk/Vacancy.aspx?ref=1034518PJ. Reference 1034518PJ should be quoted on all correspondence. You may leave the list at any time by sending the command SIGNOFF allstat to [log in to unmask], leaving the subject line blank.