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Durham University

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Funded PhD Studentship in Efficient Physical AI for Real-World Robotics at Durham University Durham University in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Fully funded PhD supported by Intel. Covers Home tuition fees and provides a tax-free stipend. Open only to UK/Home fee-status applicants.

Deadline

Jan 19, 2027

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Country

United Kingdom

University

Durham University

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Keywords

Computer Science
Electrical Engineering
Mathematics
Computer Vision
Reinforcement Learning
Robotics
Statistics
Autonomous System
Physics
ML

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About this position

Durham University is advertising a fully funded PhD studentship in Efficient Physical AI for Real-World Robotics, supported by Intel. The project sits at the intersection of computer science, robotics, machine learning, computer vision, reinforcement learning, embodied AI, and edge AI.

The research aims to develop capable, efficient, deployable Physical AI for real robots. Possible directions include robot foundation models, vision-language-action models, long-horizon reasoning and planning, data-efficient learning, continual and reinforcement learning, world models, multimodal perception, simulation-to-real transfer, compact models, efficient inference, and embedded deployment.

The studentship is based at Durham University in Durham, England, United Kingdom. The advert highlights access to Bede, a departmental GPU cluster, and robotics/sensing equipment including LiDAR, RADAR, EEG, drones, cameras, embedded devices, and robots such as Unitree platforms and unmanned ground/aerial systems. Intel collaboration focuses on efficient deployment, intelligent sensing, and Physical AI on edge devices.

Supervision is by Dr Amir Atapour-Abarghouei and Prof Toby Breckon at Durham, with Dr Samet Akcay from Intel as co-supervisor. The team’s expertise includes computer vision, machine learning, robotics, deep reinforcement learning, autonomous systems, and efficient AI.

Funding covers Home tuition fees and includes a tax-free stipend. The opportunity is open only to Home fee-status applicants, and applicants are asked to state their UK Home eligibility in all communications and applications.

Applicants should have a relevant undergraduate or master’s degree in computer science, AI, engineering, mathematics, physics, or a related area, plus strong programming skills and a clear interest in the research themes. Experience with robotics, reinforcement learning, foundation models, vision-language models, efficient inference, or embedded AI is helpful but not required.

To apply, email your CV, transcripts, and supporting documents to Dr Amir Atapour-Abarghouei at [email protected]. Applications are reviewed on a rolling basis until the position is filled. The posted closing date is 19 January 2027.

Funding details

Fully funded PhD supported by Intel. Covers Home tuition fees and provides a tax-free stipend. Open only to UK/Home fee-status applicants.

What's required

Applicants need a relevant undergraduate or master’s degree in computer science, AI, engineering, mathematics, physics or a related field; strong programming ability; interest in machine learning, computer vision, robotics, embodied AI, foundation models or autonomous systems; motivation to research and publish; and the ability to connect theory, implementation and evaluation. Experience with robotics, reinforcement learning, foundation or vision-language models, efficient inference or embedded AI is useful but not essential. Applicants must meet Durham’s English language requirements and the studentship is open only to Home fee-status applicants.

How to apply

Email your CV, transcripts and supporting documents to Dr Amir Atapour-Abarghouei. State your UK Home eligibility in all communications and applications. Applications are considered on a rolling basis until the post is filled.

More information can be found here

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