Manchester Metropolitan University
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PhD Studentship in Lightweight Trust-Aware Private Federated Learning for Secure UAV Swarms under Model Poisoning Attacks Manchester Metropolitan University in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
Doctoral teaching assistant position combining a PhD programme with a university teaching contract. Time split approximately 60% research and 40% teaching. Grade 6 salary of £31,236 and home PhD tuition fees covered for the duration of the 6-year award.
Deadline
Oct 4, 2026
Country
United Kingdom
University
Manchester Metropolitan University

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About this position
Manchester Metropolitan University in the United Kingdom is advertising a PhD studentship on Lightweight Trust-Aware Private Federated Learning for Secure UAV Swarms under Model Poisoning Attacks.
The project sits at the intersection of computer science, machine learning, cyber security, federated learning, privacy-preserving computation, edge intelligence, and UAV swarm systems. The research aims to develop lightweight, trust-aware methods that can detect suspicious model updates, reduce the impact of compromised UAVs, and preserve learning performance in the presence of poisoning, backdoor attacks, non-IID data, and unreliable communications.
Planned work includes defining a UAV-PFL threat model, building a reproducible benchmark, designing multi-evidence trust metrics, developing a privacy-aware trust-calibrated aggregation algorithm, and evaluating accuracy, attack resilience, privacy leakage, communication overhead, computation cost, and deployment feasibility.
This is a doctoral teaching assistant position combining PhD research with a teaching contract. The role is approximately 60% research and 40% teaching, with a current salary of £31,236 and home PhD tuition fees covered for the full 6-year award. The opportunity is open to home students who have the right to work in the UK; visa sponsorship is not available.
Applicants should have, or expect to obtain, an undergraduate degree and preferably a postgraduate qualification in computer science, artificial intelligence, cyber security, data science, or a closely related field. Strong programming skills, preferably in Python/C#, and experience with machine learning, deep learning, or experimental AI evaluation are preferred. Interest in secure distributed AI, federated learning, adversarial machine learning, UAV systems, or edge intelligence is also important.
To apply, complete the online application form for a part-time PhD in Computing & Digital Technology, fill in the Doctoral Project Applicant Form, and upload your CV and covering letter through the University’s Admissions Portal or send them to the PGR Admissions team by email. Quote the reference provided in the advert.
Funding details
Doctoral teaching assistant position combining a PhD programme with a university teaching contract. Time split approximately 60% research and 40% teaching. Grade 6 salary of £31,236 and home PhD tuition fees covered for the duration of the 6-year award.
What's required
Applicants should hold, or expect to obtain, an undergraduate degree and preferably a postgraduate qualification in computer science, artificial intelligence, cyber security, data science, or a closely related discipline. Essential requirements include good programming skills, preferably in Python/C#, experience with machine learning, deep learning, or experimental AI evaluation, interest in secure distributed AI, federated learning, adversarial machine learning, UAV systems, or edge intelligence, strong analytical and problem-solving skills, independent research ability, and good written communication skills for research papers and thesis chapters. Home students only; applicants must have the right to work in the UK and visa sponsorship is not available.
How to apply
Complete the online application form for a part-time PhD in Computing & Digital Technology. Fill in the Doctoral Project Applicant Form and upload your CV and covering letter in the University’s Admissions Portal, or email them to [email protected]. Quote reference SciEng-DTA Jan 2027-MR-AI Security UAV.
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