Bayesian methods for inverse problems have become increasingly popular in applied mathematics in the last decades but the theoretical understanding of the statistical performance of these methods for non-linear inverse problems is still developing. In this talk I'll consider the inverse problem of discovering the absorption term f>0 in a heat equation, with given boundary and initial value functions, from N discrete noisy point evaluations of the forward solution. I will show that for this non-linear parabolic inverse problem the optimal minimax rate can be achieved with truncated Gaussian priors.