International Association
for Cryptologic Research

This PhD studentship offers an exciting opportunity to delve into the rapidly evolving fields of Cybersecurity and Artificial Intelligence (AI). As AI technologies become increasingly integrated into enterprise systems, the need to secure these systems against sophisticated cyber threats has never been more critical. This research project at the University of Sheffield is focused on addressing these challenges by developing advanced methods to protect AI systems from cyber attacks, ensuring their reliability, integrity, and resilience in real-world environments. As a PhD candidate, you will join the Security of Advanced Systems Research Group, working on cutting-edge research that bridges the gap between AI innovation and cybersecurity. Your work will involve identifying and analyzing vulnerabilities in AI systems, creating robust models capable of withstanding adversarial attacks, and implementing state-of-the-art security measures to safeguard AI infrastructures. Additionally, you will have the opportunity to collaborate with industry leaders, contributing to the development of new cybersecurity standards for AI. This studentship is ideal for individuals passionate about AI and cybersecurity who are eager to contribute to the protection of critical technologies. The position is open to candidates worldwide, offering a unique chance to make a significant impact in a high-demand field. Research Themes Include: Analyzing vulnerabilities in AI systems Developing robust AI models resistant to adversarial attacks Implementing advanced security measures within AI infrastructures Collaborating with industry leaders to set new cybersecurity standards for AI Position Details: Starting Date: Flexible, with preferred start dates in September 2024 or January 2025. Location: Department of Computer Science, University of Sheffield, UK. Duration: 3 years Additional Note: International candidates are encouraged to apply. Please note that supplementary funding may be required to cover the tuition fee differential.