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Cybersecurity Risk Assessment and Mitigation in artificial Intelligence life cycle

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London, United Kingdom

Academic Connect
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Cybersecurity Risk Assessment and Mitigation in artificial Intelligence life cycle

About the Project

We are looking for a motivated PhD candidate to join our innovative research project focused on developing cutting-edge solutions for cybersecurity risk assessment and mitigation in the Artificial Intelligence (AI) life cycle.

In recent years, AI has seen rapid growth in its development and integration across various industries, economies, and societies worldwide. This significant progress is mainly due to significant breakthroughs in machine learning, deep learning, and the massive advancement of computational capabilities. As AI technologies have advanced and enhanced efficiency and productivity, they remain susceptible to an ever-growing number of security threats and vulnerabilities. The rapid advancement of AI therefore requires the need for a robust understanding of the evolving risks that are specifically associated with AI.

In the literature, there is no single comprehensive evaluation of AI-specific cybersecurity risks across each stage of the AI lifecycle. This project aimed to fill this gap in cybersecurity risk assessment and mitigation in the AI life cycle. This project thoroughly evaluates the potential exploitation, impact, and AI-specific cyber security risks associated with each lifecycle phase.

As a research team member, the candidate will have access to state-of-the-art research facilities and collaborate with a highly interdisciplinary team of researchers. The candidate will also have opportunities to present their research findings at top-tier conferences and publish in high-impact academic journals.

The ideal candidate should have or expect to achieve at least a 2:1 Honours degree (or equivalent) in Computer Science, Cyber Security, or a related subject. Relevant experience in AI will be an advantage. The applicant should be familiar with C/C++, various scripting languages, machine learning and more particularly deep learning (PyTorch, TensorFlow, etc.) and with the Linux environment.

Funding Notes

there is no funding for this project

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