This project develops Bayesian methods for equivariant neural networks, building on prior work showing that data augmentation can induce group equivariance in variational Bayesian neural networks (arXiv:2606.26273). Empirically validating our theoretical results requires training equivariant network variants under group data augmentation across multiple random seeds, dataset sizes, and symmetry groups, which motivates the GPU allocation requested here. The project is carried out at the Department of Mathematical Sciences, Chalmers University of Technology, under the supervision of Jan Gerken.