Job Description

The emergence of edge AI systems--AI deployed on resource-constrained, often battery-powered, devices at the edge of the network--presents critical security challenges. These systems are increasingly vulnerable to hardware-level threats, including side-channel attacks, fault injections, etc., particularly when optimized for performance.
This Research Fellow position focuses on AI security in the context of hardware-constrained edge devices, investigating how hardware acceleration can be leveraged by adversaries to compromise AI systems' robustness. The role involves designing secure AI accelerators, analyzing attack surfaces introduced by approximation, and developing a performance-security trade-off framework to guide secure AIoT deployment.
About the person:

• The successful candidate will have, or be close to obtaining, a PhD in computer science, engineering, mathematics, or a related physical sciences discipline, with research expertise in areas such as hardware-aware AI security, approximate computing, or secure embedded AI systems.
• They will demonstrate a strong track record of high-quality research in machine learning/AI and/or embedded systems, evidenced by publications in leading conferences and journals.
• The candidate will have hands-on experience with Edge AI and embedded systems security, as well as a solid grounding in AI security and Trustworthy AI.
• They will be proficient in Python and ideally familiar with hardware design (Verilog/VHDL), FPGA-based acceleration, etc.
• Experience with deep learning frameworks like PyTorch, Keras, or TensorFlow, and tools such as Jupyter Notebook, is expected.
• A strong foundation in core machine learning theory--including statistics, optimization, and linear algebra--is desirable.
• The ideal candidate will have a proven ability to independently develop and execute research plans and a track record of successful collaboration with industry partners.

To be successful at shortlisting stage, please ensure you clearly evidence in your application how you meet the essential and, where applicable, desirable criteria listed in the Candidate Information.
This post is available for 12 months. Fixed term contract posts are available for the stated period in the first instance but in particular circumstances may be renewed or made permanent subject to availability of funding.
What we offer:
Beyond a competitive salary, the University offers an attractive benefits package including a holiday entitlement of up to 8.4 weeks a year, pension schemes and development opportunities. We support staff wellbeing with flexible working options, work-life balance initiatives and support for physical and mental health. You can find more detail on all of this and more at
Queen's University is committed to promoting equality of opportunity to all. We subscribe to Equality Charter Marks such as the Diversity Charter Mark NI and Athena Swan and have established staff networks such as iRise (Black, Asian, Minority Ethnic and International Staff Network) and PRISM (LGBTQ+) which help us progress equality.
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