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: Technical University of Denmark
The University is an exceptional employer, offering a supportive and collaborative work environment that prioritises employee well-being and professional growth. With the flexibility of hybrid working and generous leave entitlements, including 36 days off, staff are encouraged to maintain a healthy work-life balance while contributing to the academic success of students in the Faculty of History.
Contact Details:
Technical University of Denmark Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land PhD Studentship: Resilient Federated Learning for Autonomous Systems under Distribution Shifts in Sheffield
✨Join Data-Science Meetups
Get yourself along to local data-science meetups or workshops. They're goldmines for networking, and you'll learn from industry pros who might just point you in the direction of internships. Plus, discussing the latest trends with like-minded individuals can really amp up your game.
✨Utilise University Career Services
Check in with your uni's career services since they often have connections with companies looking for interns. They might even organise information sessions with firms, which can be a great chance for you to learn more about potential internships and make some key contacts.
✨Show Off Your Stuff on GitHub
If you're into data science, having a GitHub profile with your projects is essential. Make sure your portfolio is public and showcases your best work! Recruiters love to see your coding skills and problem-solving approach, and it’s a brilliant way to stand out.
✨Apply Directly on Our Website
Don’t forget to check out the internships listed on our site! It's always a good idea to apply directly through our website because it makes your application easier for our team to find, and you might just catch the hiring manager’s eye by showcasing exactly what you're passionate about in data science.
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 Technical Skills:For a data science internship, we want to see those analytical skills shine! List your programming languages, like Python or R, and make sure to highlight any relevant projects or courses you've completed. If you've dabbled with tools like Pandas, NumPy, or machine learning algorithms, don’t hold back – include those in your CV!
Share Your Curiosity in Your Cover Letter:As an intern, your motivation and eagerness to learn are key! In your cover letter, talk about specific data science concepts that excite you and how this internship at Technical University of Denmark will help you grow. Share what you hope to achieve and how you plan to tackle real-world data problems - we love enthusiasm!
Include Any Relevant Certifications:If you've earned any certifications, such as from Coursera or DataCamp, make sure to include these in your application. They show us that you're proactive and committed to expanding your data science skillset. This could make a real difference in how we assess your application!
Keep It Relevant and Concise:Remember, as an intern, you don’t need to have decades of experience. Focus on showcasing relevant coursework, personal projects, or even related volunteer work in data science. Keep your CV and cover letter concise but impactful – we appreciate clear and straightforward communication!
How to prepare for a job interview at Technical University of Denmark
✨Brush Up on Your Coding Skills
As a data science intern, you might get grilled on your programming skills. Expect to tackle some coding challenges using languages like Python or R. We recommend practising basic algorithms or data manipulation tasks so you can show off your tech skills with confidence.
✨Show Off Your Projects
Prepare to discuss any projects you’ve done, whether in your studies or on your own time. Having a strong portfolio of data analyses or machine learning models will really set you apart. We can use platforms like GitHub to showcase your work to impress Technical University of Denmark.
✨Know Your Stats and ML Basics
Brush up on your statistics and machine learning concepts because interviewers love to dig into this! Be ready to explain your understanding of algorithms or how you would approach a given data problem. This will highlight your theoretical background alongside your practical skills.
✨Be Eager to Learn and Adapt
Internships are all about potential and growth. Make sure you convey your eagerness to learn and adapt to new tools or methodologies. Show Technical University of Denmark that you’re not just looking for experience, but that you're keen to contribute and grow within the team.
