Careful sensor placement is crucial in electromagnetic imaging experiments as it significantly impacts the quality and accuracy of the measurements. This study examines the placement of a network of sensors to advance the Bayesian learning with the aim of achieving a minimal level of uncertainty in a qualitative imaging regime. The quality of the measured data, associated with a network of sensors, is assessed by computing the expected Kullback-Leibler divergence between the prior and the posterior distributions, wherein the Laplace approximation is invoked to reduce the associated computational cost. The numerical experiment is carried out to evaluate various sensor placement scenarios to identify the network geometry that can enhance the quality of inversion.