Estimating tissue properties and synthesizing contrasts can ease the need for long acquisitions and have many clinical applications. In this work we propose an unsupervised deep-learning strategy that employs the FLASH MRI model. The method jointly estimates the T1, T2* and PD tissue parameter maps with the goal to synthesize physically plausible FLASH signals. Our approach is additionally trained for random acquisition parameters and generalizes across different acquisition protocols and provides improved performance over fixed acquisition based training methods. We also demonstrate the robustness of our approach by performing these estimation with as low as three input contrast images.