Many Parallel MRI algorithms (e.g. Sensitivity Encoding (SENSE)) require knowledge of the receiver coil sensitivity maps. Magnetic field strength is an important factor in defining the sensitivity maps of the receiver coils in MRI. This paper presents a method to estimate the receiver coil sensitivity maps of a higher magnetic field strength scanner utilizing a deep learning network (denoted as ResU-Net-34), initially trained on the receiver coil sensitivity maps of a lower field strength scanner using transfer learning. SENSE reconstruction results show a successful domain transfer between the receiver coil sensitivities of different magnetic field strengths with the proposed method.