Connected and autonomous vehicles are set to revolutionise our transportation and re-shape our cities. They will prevent accidents, reduce parking space requirements, lower congestion and pollution. But in order to achieve this, they need several sensors and wireless interfaces which connect them with other vehicles, consumer devices, infrastructure and the Internet. This connectivity adds great functionality but it also introduces a myriad of security and privacy threats. Safety critical functionality in the vehicle is controlled by a multitude of Electronic Control Units (ECUs) which are fully programmable. As vehicles become more programmable, complex and interconnected, they also become more vulnerable to cyber attacks. The main goal of this fellowship is to secure connected and autonomous vehicles, making them resilient to this type of attacks. We will achieve this goal by developing techniques to secure each component of the vehicle's electronic architecture: ensuring that each ECU only executes code that is suitably authenticated; using model learning techniques to develop a framework for automated security testing of ECUs in a way that it scales; securing the vehicle's sensors such as radar, lidar and optical cameras against signal spoofing, tampering and denial of service attacks which would cause them to output inaccurate readings; and improving the communication protocols between vehicles and between the vehicles and the infrastructure in order to provide authenticity, non-repudiation and privacy while complying with stringent real-time constraints.