职位信息
Funding provider(s): EPSRC DTP
Edge devices, integral to the Internet of Things (IoT) and modern smart applications, have increasingly relied on artificial intelligence (AI) for efficient operation and decision-making. However, these AI-based systems are vulnerable to adversarial attacks, which can compromise their functionality and security.
This project aims to investigate the vulnerability of machine learning models deployed on edge devices and develop robust adversarial learning techniques to defend against such attacks. The focus will be on identifying potential weaknesses in current edge computing frameworks and leveraging adversarial learning, edge computing and game theory to create novel methods to enhance their resilience.
This includes developing algorithms capable of detecting and mitigating adversarial attacks, ensuring that edge devices can maintain secure and reliable operations even under hostile conditions. Our research will cover various applications of edge devices, including real-time data processing, autonomous decision-making, and network intrusion detection.
By addressing these areas, we aim to create a framework that not only secures individual edge devices but also enhances the overall security of interconnected IoT systems. The outcomes of this project will provide critical insights and methodologies for deploying secure, AI-driven edge solutions, thereby fostering safer and more reliable IoT ecosystems.
Candidates must hold an UK Bachelor degree with a minimum of Upper Second Class honours in Computer Science, Mathematics or a closely related discipline or overseas Bachelor degree deemed equivalent to UK Bachelor (by UK ECCTIS) and achieved a grade equivalent to UK Upper Second Class honours in Computer Science, Mathematics or a closely related discipline.
Or an appropriate master’s degree with a minimum overall grade at ‘Merit’ (or Non-UK equivalent as defined by Swansea University) – see country specific qualifications
IELTS 6.5 Overall (with no individual component below 6.0) or Swansea University recognised equivalent.
Full details of our English Language policy, including certificate time validity, can be found here. If you have any questions regarding your academic or fee eligibility based on the above, please email pgrscholarships@swansea.ac.uk with the web-link to the scholarship(s) you are interested in.
Please note that the programme requires some applicants to hold ATAS clearance; further details on ATAS scheme eligibility are available on the UK Government website.
ATAS clearance IS NOT required to be held as part of the scholarship application process. Successful award winners (as appropriate) are provided with details as to how to apply for ATAS clearance in tandem with a scholarship course offer.
This scholarship covers the full cost of tuition fees and an annual stipend at UKRI rate (currently £19,237 for 2024/25).
Additional attending conferences expenses of up to £1,000 per year will also be available.
EPSRC DTP studentships are available to home and international students. Up to 30% of our cohort can comprise international students, once the limit has been reached, we are unable to make offers to international students.
To apply, please complete your application online using link below: https://www.swansea.ac.uk/postgraduate/scholarships/research/fse-epsrc-phd-artificial-intelligence-2024-rs728-.php
If you have any questions regarding this position, please feel free to contact Dr. Lu Zhang (lu.zhang@swansea.ac.uk).
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@留德华叫兽：系美国Clemson大学数学硕士(运筹学方向)、Ph.D. candidate，欧盟玛丽居里学者，德国海德堡大学数学博士(离散优化、图像处理)，读博期间前往意大利博洛尼亚大学、IBM实习半年，巴黎综合理工访问一季。现任德国无人驾驶资深研发工程师。
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