PhD Studentship: Efficient Physical AI: Data-Efficient Training and Edge Deployment of Compact Robot Foundation Models (Robotics, Deep learning, Computer vision)

Number of awards: 1

Award information: Fully funded PhD studentship covering Home tuition fees and a tax-free stipend. Available to Home fee-status applicants only due to funding structure. Delivered in collaboration with Intel, including industrial co-supervision. The student will work with Durham University academic supervisors and an Intel co-supervisor on compact Physical AI, data-efficient robot learning and edge deployment.

Closing date: 15 August

Overview
Robotics is entering a new phase where foundation models connect perception, language and action. Vision-language-action models, robot foundation models and world models enable robots that understand instructions, interpret scenes and generate behaviours. However, most systems require huge datasets, extensive teleoperation, expensive compute and large models, limiting real-world deployment.

This PhD focuses on efficient Physical AI, emphasising data-efficient training, reinforcement learning, continual adaptation and edge deployment of compact robot models. The challenge is building capable robot intelligence with limited real robot data and compute. Interaction data is costly and slow to collect, while deployment requires real-time performance (10–50 Hz) on hardware with constrained memory, power and compute.

The project will investigate training, adapting and deploying compact Physical AI models efficiently. Work may cover vision-language-action models, imitation and reinforcement learning, world models, multimodal perception, model compression and edge inference. A key aim is enabling robots to improve beyond initial demonstrations via feedback, interaction and continual learning. It will explore converting human videos, simulation, web-scale data and unstructured experience into supervision and reward signals through relabelling, offline RL, policy distillation and human-to-robot transfer.

Possible directions include data-efficient learning, RL for Physical AI, continual adaptation, compact foundation models, edge-deployable VLAs, world models, and hybrid architectures evaluated by capability per parameter, watt and training data. The vision is practical robot intelligence without enormous datasets, large fleets or unrestricted compute, for use in labs, factories, farms, homes and inspection settings.

Durham offers strong facilities: Bede HPC (128 GPU), GPU cluster (90+ GPU), LiDAR, RADAR, drones, cameras, embedded devices and robots (including Unitree G1 humanoid, quadrupeds, UGVs and aerial platforms). Intel collaboration provides industrial insight into edge AI and deployable Physical AI.

Supervision: Dr Amir Atapour-Abarghouei, Dr Chris Willcocks, Prof Toby Breckon (Durham) and Dr Samet Akcay (Intel Principal Engineer, Edge Computing). The team publishes in top venues (CVPR, ICCV, ECCV, ICML). Students receive regular guidance, research training, publication support, hardware/compute access and Intel engagement opportunities.

Entry requirements: Relevant undergraduate or master’s degree in computer science, AI, engineering, mathematics, physics or related field; strong programming skills; interest in ML, computer vision, robotics, embodied AI or autonomous systems; motivation for independent research and high-quality publications; ability to combine theory, implementation and evaluation; and meet Durham’s English language requirements. Experience in robotics, RL, foundation/vision-language models or efficient inference is desirable but not essential.

Durham University is a leading Russell Group institution in a historic North East England city with excellent quality of life. Intel leads in computing innovation for efficient, real-world edge AI and robotics.

How to apply
Email your CV, transcripts and supporting documents to Dr Amir Atapour-Abarghouei at amir.atapour-abarghouei@durham.ac.uk for an initial discussion. Applications must be sent before 15 August. Interviews will follow shortly after for a proposed October 2026 PhD start date.

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