The goal of the PhD project is to contribute to the development of innovative Genome-Environment Association (GEA) and Genomic Offset (GO) procedures that will build on recent advances in optimization and statistical inference. More specifically the recruited PhD student will consider variational inference approaches for GEA and stochastic optimization to speed up the inference, with the objective of scaling up to modern genomic datasets that may involve hundreds of populations. He/she will also develop probabilistic GO models inspired from the Redundancy Analysis approach and extend it by introducing Neural Networks in order to handle non-linear relationships between covariates and response variables. To this aim, the PhD student will join a consortium of researchers issued from different disciplines with a long experience in interdisciplinary projects. The developed methodology will be applied to public datasets for benchmarking purposes, as well as, to an innovative set of two datasets corresponding to a domestic plant (maize) and its wild “ancestor” (teosinte). This will enable us to investigate how adaptation of crop wild relatives to dry environments could be informative about the response of maize to a drier climate.
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