Applications are invited for the following 3-year PhD position at Aarhus University, Denmark. Genomic prediction models are statistical models to predict complex traits on the basis of genomic data, usually from single nucleotide polymorphism (SNP) arrays. These models are of central importance in genomic selection, in which the predictions are used to accelerate animal and plant breeding programs; they are also important in other areas, for example, medical genetics. The models are theoretically and computationally challenging due to the high number of predictor variables (>50K). To date some of the most successful methods use Bayesian modelling techniques, in which prior distributions are set on the SNP effects and other parameters. The goal of this project is to study whether these models can be improved by taking linkage disequilibrium (LD) between SNPs into account. Associations between SNPs induce correlations between SNP effect estimates, and it is hoped that accounting for LD structure will improve prediction accuracy, generate more precise estimates of location of large SNP effects, and improve the computational efficiency of the algorithms. For more information see http://agrsci.au.dk/om_djf/jobs/phd_stillinger/ Best Regards David Edwards [[alternative HTML version deleted]]