At a Glance
- Tasks: Develop resilient federated learning algorithms for autonomous systems in diverse environments.
- Company: University of Sheffield, collaborating with Defence Science and Technology Laboratory.
- Benefits: Fully funded PhD with a £25,000 tax-free stipend and annual increases.
- Other info: Join a leading research group with access to modern computing facilities and collaborative opportunities.
- Why this job: Make a real impact on safety-critical applications like environmental monitoring and emergency response.
- Qualifications: First-class degree or strong upper-second-class degree in relevant fields; programming skills required.
The predicted salary is between 25000 - 25000 £ per year.
Applications are invited for a fully funded PhD studentship in the School of Electrical and Electronic Engineering at the University of Sheffield, in collaboration with the Defence Science and Technology Laboratory (Dstl). Due to funding restrictions, the position is open to candidates eligible for UK home student fees. It offers an enhanced tax-free stipend of approximately £25,000 per year, subject to annual increases.
About the Project (Background & Methodology)
Autonomous systems such as drone fleets, mobile robots, and sensor networks increasingly use federated learning (FL) to train shared models without centralising raw data. In practice, however, individual platforms operate under diverse environmental conditions, sensor calibrations, and system configurations. These differences introduce distribution shifts that can degrade model performance and reliability over time, particularly in "one-to-many" supervision settings where a single human operator oversees multiple agents. Ensuring resilience in such scenarios is critical for safety-critical applications including environmental monitoring, infrastructure inspection, and emergency response.
This PhD will develop a mathematical and algorithmic framework to assess and improve the resilience of FL-enabled autonomous systems under such heterogeneity, explicitly incorporating the human in the loop. The project will draw on information theory, machine learning, and control to analyse how local variability, sensor drift, and platform differences affect both global model performance and human supervisory factors such as workload and intervention behaviour. Building on this, the research will design robust FL algorithms and adaptive supervisory strategies, including dynamic aggregation, confidence-based thresholds, and escalation mechanisms. A comparative study across civil and defence scenarios, using real and synthetic data, will identify both generalisable and domain-specific resilience mechanisms.
School of Electrical and Electronic Engineering at the University of Sheffield
A leading centre for machine learning, robotics, and autonomous systems. The student will join a research group working at the interface of machine learning, control and information theory, with opportunities to collaborate with partners in robotics, autonomous systems and AI safety. Access to modern computing facilities and experimental platforms (e.g. robotic testbeds or simulators) will be available depending on the final focus of the work.
Defence Science and Technology Laboratory (Dstl)
As the Ministry of Defence (MOD)’s in-government science and technology organisation, Dstl provides unique expertise, insight and innovation to maintain UK warfighting readiness in an increasingly dangerous and complex world. As MOD science and technology leaders, Dstl provides expert advice, analysis and capability across a wide range of applications including Robotics & Autonomous Systems, AI and Data Science.
Eligibility and Desired Background
Applicants should hold (or expect to obtain) a first-class or strong upper-second-class degree, or a Master’s degree, in a relevant discipline such as Control/Systems Engineering, Electrical or Electronic Engineering, Computer Science, Applied Mathematics or a closely related field. A strong mathematical background (probability, linear algebra, optimisation) and proficiency in programming (preferably Python/Matlab) are essential. Prior exposure to one or more of: machine learning, reinforcement learning, robotics/autonomous systems, information theory, or human-machine interaction will be an advantage.
Inquiries
Interested candidates are encouraged to contact Dr Iñaki Esnaola or Dr Morgan Jones by email to discuss the position informally and should include a brief CV detailing their suitability for the role. Formal applications should be made through the University of Sheffield application portal, and include a CV and covering letter.
PhD Studentship: Resilient Federated Learning for Autonomous Systems under Distribution Shifts in Sheffield employer: University of Sheffield
The University of Sheffield offers an exceptional environment for PhD candidates, particularly in the School of Electrical and Electronic Engineering, where cutting-edge research in machine learning and autonomous systems thrives. With a fully funded studentship that includes a generous tax-free stipend, students benefit from access to modern facilities and collaborative opportunities with leading experts at the Defence Science and Technology Laboratory. The supportive work culture fosters innovation and personal growth, making it an ideal place for aspiring researchers to develop their skills and contribute to impactful projects.
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We think this is how you could land PhD Studentship: Resilient Federated Learning for Autonomous Systems under Distribution Shifts in Sheffield
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We think you need these skills to ace PhD Studentship: Resilient Federated Learning for Autonomous Systems under Distribution Shifts in Sheffield
Some tips for your application 🫡
Show Off Your Data Skills:Make sure to highlight your data-related skills clearly in your CV. If you've worked on any projects using Python or R, be sure to mention them. Also, if you've dabbled in machine learning, include that too—employers love a hands-on learner!
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Personalise Your Cover Letter:Use your cover letter to show how passionate you are about data science. Describe why you're excited about the opportunity at University of Sheffield and how the role aligns with your career goals in this dynamic field. We want to see your enthusiasm and potential to grow.
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How to prepare for a job interview at University of Sheffield
✨Show Off Your Data Skills
Make sure you're ready to chat about the tools we use in data science, like Python, R, or SQL. Brush up on key concepts like data cleaning, analysis, and visualisation — interviewers often delve into these areas to see how you think and approach problems.
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Use this opportunity to find out more about the team’s projects and tools. Questions like ‘What data challenges are you currently facing?’ can demonstrate our interest while also helping us understand how we might fit into University of Sheffield’s culture and workflow.
