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- Tasks: Research AI security for edge devices, focusing on hardware vulnerabilities and secure AI accelerator design.
- Company: Join Queen's University Belfast, a leading institution in research and innovation.
- Benefits: Enjoy flexible working, generous holiday, pension schemes, and personal development opportunities.
- Why this job: Be at the forefront of AI security, tackling real-world challenges in a collaborative environment.
- Qualifications: PhD in relevant fields, experience with AI frameworks, and strong research background required.
- Other info: This is a 12-month fixed-term contract with potential for renewal.
The predicted salary is between 36000 - 60000 £ per year.
Organisation/Company Queen\’s University Belfast Research Field Computer science » Other Researcher Profile Leading Researcher (R4) Established Researcher (R3) Country United Kingdom Application Deadline 25 Aug 2025 – 00:00 (UTC) Type of Contract To be defined Job Status Negotiable Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No
Offer 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.
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 candidate will ideally 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.
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 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 Human Resources
Queen\’s University is committed to promoting equality of opportunity to all.
For further information on our commitment to Equality, Diversity and Inclusion, please visit Diversity .
If you are an international applicant and don\’t already hold a visa that permits you to take up the role you are applying for, please use the information provided on our website to self-assess whether the University is likely to be able to support a visa application – Staff Support
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Postdoctoral Research Fellow in AI security employer: European Commission
Contact Detail:
European Commission Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Postdoctoral Research Fellow in AI security
✨Tip Number 1
Network with professionals in the field of AI security, especially those who have experience with edge devices. Attend relevant conferences or workshops to meet potential collaborators and mentors who can provide insights into the role and the research environment at Queen's University Belfast.
✨Tip Number 2
Familiarise yourself with the latest research and developments in hardware-aware AI security and approximate computing. This will not only enhance your understanding but also allow you to engage in meaningful discussions during interviews, showcasing your passion and knowledge about the subject.
✨Tip Number 3
Demonstrate your hands-on experience with deep learning frameworks like PyTorch or TensorFlow by preparing a portfolio of projects or research that highlights your skills. Be ready to discuss these projects in detail, as practical experience is highly valued for this position.
✨Tip Number 4
Prepare to articulate your research plans and how they align with the goals of the AI security team at Queen's University. Having a clear vision of your research direction and its potential impact will help you stand out as a candidate who can contribute significantly to the team.
We think you need these skills to ace Postdoctoral Research Fellow in AI security
Some tips for your application 🫡
Understand the Role: Thoroughly read the job description for the Postdoctoral Research Fellow in AI security at Queen's University Belfast. Make sure you understand the specific requirements and responsibilities, especially regarding hardware-aware AI security and edge devices.
Highlight Relevant Experience: In your application, clearly demonstrate your research expertise in machine learning, AI, and embedded systems. Include specific examples of your work with deep learning frameworks like PyTorch or TensorFlow, and any hands-on experience with Edge AI.
Address Essential Criteria: Ensure that your application explicitly addresses the essential and desirable criteria listed in the Candidate Information. Use bullet points or a clear structure to make it easy for reviewers to see how you meet these requirements.
Craft a Strong Cover Letter: Write a compelling cover letter that not only summarises your qualifications but also expresses your enthusiasm for the role and the research being conducted at Queen's University. Tailor it to reflect your understanding of the challenges in AI security for edge devices.
How to prepare for a job interview at European Commission
✨Showcase Your Research Expertise
Be prepared to discuss your previous research in detail, especially any work related to AI security, hardware-aware AI, or embedded systems. Highlight your publications and how they relate to the role, as this will demonstrate your capability and fit for the position.
✨Demonstrate Technical Proficiency
Familiarise yourself with deep learning frameworks like PyTorch, Keras, or TensorFlow, and be ready to discuss your experience with them. If you have worked with tools like Jupyter Notebook or have hands-on experience with Edge AI, make sure to mention these during the interview.
✨Understand the Role's Challenges
Research the specific security challenges faced by edge AI systems, such as side-channel attacks and fault injections. Being able to articulate these issues and propose potential solutions will show your understanding of the field and your proactive approach.
✨Prepare for Collaboration Questions
Since collaboration with industry partners is essential, think of examples from your past experiences where you successfully worked in a team or partnered with external organisations. This will highlight your ability to contribute effectively in a collaborative research environment.
