Deep Learning (DL) has become a fundamental building block of learning-enabled autonomous systems. Notwithstanding its great potential, employing DL in safety- and security-critical applications, including robots providing service in healthcare facilities or drones used for inspection and maintenance, raises significant trustworthiness challenges. Within the European project SESAME, we develop a model-based approach supporting the systematic engineering of dependable learning-enabled robotic systems. In this talk, we will overview recent advances made by the project team to provide assurances for the trustworthy, robust and explainable operation of DL, focusing particularly on techniques for deep learning testing and uncertainty analysis.