AI and Robotics: Fully Funded EPSRC PhD Studentship: Human-in-the-Loop Robot Control and Learning based on AI methods (RS724)

Funding provider(s): EPSRC DTP 25/26 Fully Funded Studentship

Subject area(s): AI and Robotics

Project start date(s): 1 October 2025 (Enrolment open from mid-September)

Supervisors:

Primary Supervisor - Dr Chunxu Li

Secondary Supervisor - Professor Cinzia Giannetti

Aligned programme of study: PhD in Mechanical Engineering

Mode of study: Full-time

Project description:

This project explores advanced Human-in-the-Loop (HITL) methodologies for robot control and learning, leveraging state-of-the-art AI techniques to enhance human-robot collaboration. Traditional autonomous robot systems often lack flexibility in dynamic and complex environments, which limits their adaptability and decision-making capabilities. By incorporating HITL mechanisms, this project aims to enable robots to learn from human feedback and improve their performance in real-time.

The project focuses on developing AI-driven algorithms that allow robots to interpret human input, adapt to new tasks, and refine their actions through continuous learning. Key AI methods include reinforcement learning, neural networks, and computer vision for recognizing human gestures, feedback, and preferences. This approach has broad applications, from industrial automation to assistive robotics, where real-time adaptability and safety are critical. The goal is to create robots that act autonomously yet are responsive to human guidance, leading to more efficient, reliable, and safe human-robot interactions in various real-world settings.

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. 

The studentship covers tuition fees and an annual tax-free living stipend in line with UKRI minimum rates (currently £19,237 for 2024/25).

Funds for other expenses (e.g. conferences, fieldwork): £1,000 for the duration of funded period

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.

We are still accepting applications from international applicants.

International students will not be charged the fee difference between the UK and international rate. Applicants should satisfy the UKRI eligibility requirements.

To apply, please complete your application online with the following information:

1. Course choice – please select:  

   Mechanical Engineering/ PhD / Full-time / 3 Years / October

   In the event you have already applied for the above programme previously, the application system may issue a warning notice and prevent application, in this event, please email pgrscholarships@swansea.ac.uk where staff will be happy to assist you in submitting your application.

2. Start year – please select 2025
3. Funding (page 8) –

• ‘Are you funding your studies yourself?’ – please select No
• ‘Name of Individual or organisation providing funds for study’ – please enter ‘RS724 - AI methods'

*It is the responsibility of the applicant to list the above information accurately when applying, please note that applications received without the above information listed will not be considered for the scholarship award.

As part of your online application, you MUST upload the following documents (please do not send these via email):

• CV
• Degree certificates and transcripts
• A cover letter
• Two references
• Evidence of meeting English Language requirement (if applicable)
• Copy of UK resident visa (if applicable)
• Confirmation of submission of EDI Form

*External Partner Application Data Sharing – Please note that as part of the scholarship application selection process, application data sharing may occur with external partners outside of the University, when joint/co- funding of a scholarship project is applicable.