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

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 dependence on very large models, extensive data, costly co...

Opportunity details

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 dependence 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,...

Eligibility

Open only to Home fee-status applicants. 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. 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. 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.

Requirements

Open only to Home fee-status applicants. 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. 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. 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.

Funding and benefits

Funding eligibility: UK Students. Funding amount: Covers Home tuition fees and a tax-free stipend..

Required documents

CV

How to apply

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. 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. Email CV, transcripts and supporting documents to Dr Amir Atapour-Abarghouei (amir.atapour-abarghouei@durham.ac.uk). Applications are considered on a rolling basis until the post is filled.

Open the official application information

Verify the deadline and final conditions on the official provider page before submitting an application.