Efficient Physical AI for Real-World Robotics – Funded PhD Studentship

Durham University; North East; On-site; GBP 18,000 - 22,000; Full time

Benefits offered by this job

Covers Home tuition fees

Tax-free stipend

Job summary

Durham University invites applications for a fully funded PhD supervised by Intel. The project develops Physical AI for real robots, integrating perception, reasoning, learning and action with cutting-edge models and robotics hardware.

Co-supervision from Durham academics and Intel engineers supports a broad, publication-focused approach. Applicants should hold a relevant degree, show strong programming and interest in ML, CV, robotics, embodied AI, and foundation models.

Qualifications

• Applicants need a relevant undergraduate or master’s degree (computer science, AI, engineering, mathematics, physics or related).
• Strong programming ability and interest in machine learning, computer vision, robotics, embodied AI, foundation models or autonomous systems.
• Motivation to research and publish; ability to link theory, implementation and evaluation; Durham English language requirements.
• Experience in robotics, reinforcement learning, foundation or vision-language models, efficient inference or embedded AI is useful, not essential.

Responsibilities

• Develop capable, efficient, deployable Physical AI for real robots.
• Explore topics such as robot vision-language-action models and real-time planning.
• Pursue data-efficient learning with demonstrations and interaction; train and evaluate models.
• Collaborate with Durham and Intel supervisors; contribute to research publications.

Skills

Strong programming

Machine learning

Computer vision

Robotics

Reinforcement learning

Embodied AI

Foundation models

Autonomous systems

Education

Undergraduate or Master’s degree

Tools

GPU clusters

Job description

Fully funded PhD supported by Intel

[Covers Home tuition fees and a tax-free stipend. Open only to Home fee-status applicants. Co-supervised by Durham academics and an Intel co-supervisor. State your UK Home eligibility in all communications and applications.]

Physical AI moves beyond analysis to systems that perceive, reason, learn and act in the world, combining foundation models, computer vision, machine learning and robotics. Deployment is limited by depending on very large models, extensive data, costly compute, cloud inference and large-scale robot data collection, while robots must run continuously under constraints on compute, memory, latency, power and training data.

This PhD will develop capable, efficient, deployable Physical AI for real robots, shaped to the student’s strengths. Topics may include: robot foundation and vision-language-action models; efficient reasoning and planning for long-horizon, real-time tasks; data-efficient learning with fewer demonstrations and less physical interaction; reinforcement and continual learning for adaptation and recovery; world models and predictive learning; multimodal perception; learning from simulation, synthetic data, human video, demonstrations and unstructured experience; compact models (compression, distillation, efficient inference); edge AI on embedded hardware; hybrid intelligence linking reactive control with higher-level reasoning; and the gap between simulation, benchmarks and physical platforms.

Research may cover new algorithms, foundation-model evaluation, learning pipelines, benchmarks, robot integration, or computational and practical limits. Focus may be machine learning, computer vision, reinforcement learning, robotics, embodied AI, efficient inference, or a mix. The aim is capable, efficient, adaptive, reliable, practical systems.

Durham hosts Bede (UK regional supercomputer), a departmental GPU cluster, and labs with LiDAR, RADAR, EEG, drones, cameras, embedded devices and robots, such as the Unitree G1 humanoid, Unitree Go2 quadruped, unmanned ground vehicles and aerial platforms. Intel collaboration targets edge AI, compact models, efficient deployment, intelligent sensing and Physical AI.

Supervisors: Dr Amir Atapour-Abarghouei and Prof Toby Breckon (Durham); Dr Samet Akcay, Principal Engineer, Edge Computing, Intel Client Computing and Physical AI. Expertise spans computer vision, machine learning, robotics, deep reinforcement learning, autonomous systems and efficient AI, with papers in CVPR, ICCV, ECCV, ICML and related venues.

Training covers regular supervision, help building an independent profile, training in writing, publication and presentation, access to GPUs, robots and sensors, and contact with Intel. Style: independent, problem-driven research in a supportive setting.

Durham is a Russell Group university in historic North East England, with a collegiate system, strong community and affordable living costs. Intel’s Edge Computing team brings efficient AI and robotics intelligence to devices outside the cloud.

Applicants need a relevant undergraduate or master’s degree (computer science, AI, engineering, mathematics, physics or related); strong programming; interest in machine learning, computer vision, robotics, embodied AI, foundation models or autonomous systems; motivation to research and publish; ability to link theory, implementation and evaluation; and Durham’s English language requirements. Robotics, reinforcement learning, foundation or vision-language models, efficient inference or embedded AI experience is useful, not essential.

Subjects: Computer Science, Artificial Intelligence, Mathematics, Machine Learning, Computer Vision, Robotics, Engineering.

Covers Home tuition fees and a tax-free stipend.