Forwarded as requested ---------------------- This is an outstanding opportunity for two ambitious individuals to work in an area of advanced statistical research that will have the potential to directly impact practice in clinical diagnostics. These positions present a unique setting to work with analytical chemists pioneering the use of nano-particle assemblies in clinical diagnostics and with the clinicians who wish to translate this research into clinical practice.
Our challenge at Warwick is to develop the essential novel statistical methods to:- (1) solve the inverse problem of frequency spectra deconvolution, when all that is available is the contaminated and mixed frequency response from a nanoparticle assembly that has bound to an unknown number of biomarkers. (2) To provide probabilistic assessments that will support clinical reasoning and decision making by the integration of diverse sources of evidence, in the form of measured biomarkers and clinical indicators, in probabilistically assessing the likelihood of disease states of an individual patient. Research in statistical modelling and inference is at the core of this programme of research and a Bayesian framework will be adopted providing the successful candidate with a wide range of methodological challenges and opportunities to undertake exciting high impact research work. Department of Statistics Salary £28,132-£36,661pa University of Warwick, Coventry Fixed Term Contract for 4.0 Years The Project For further information about the programme of research, please see the overall project description at the EPSRC website http://gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/L014165/1. You will work with Professor Mark Girolami on the EPSRC funded programme grant "In Situ Nanoparticle Assemblies for Healthcare Diagnostics and Therapy" to develop novel statistical methodology. You must hold or be near completion of a PhD in computational statistics or a closely related discipline such as inverse problems, biostatistics, pattern recognition, signal processing or probabilistic machine learning. A good knowledge of computational Bayesian statistical techniques is essential as are strong programming skills. You will have demonstrated potential for excellence in research and an emerging track record of publication in high quality, peer reviewed journals. Informal enquiries can be addressed to Professor M.A.Girolami (m.girol...@warwick.ac.uk). Start date: By agreement, on or after 1 June 2014 ------------------------------------------- Professor M.A. Girolami FRSE FIET EPSRC Established Career Research Fellow Chair of Statistics Department of Statistics University of Warwick Coventry, CV4 7AL Tel: +44(0)24 7657 4808 Fax: +44(0)24 7652 4532 email: m.girol...@warwick.ac.uk web: http://warwick.ac.uk/mgirolami ------------------------------------------- _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
