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Durham University

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PhD Studentship in Efficient Long-Horizon Task Execution in Physical AI (Deep Learning, Computer Vision, Robotics) Durham University in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Full funding available

Deadline

Aug 15, 2026

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Country

United Kingdom

University

Durham University

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Keywords

Computer Science
Electrical Engineering
Deep Learning
Mathematics
Computer Vision
Robotics
Autonomous System
Physics

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About this position

Durham University is offering a fully funded PhD studentship in Physical AI, with a focus on efficient long-horizon task execution for robots and intelligent autonomous systems. The project sits at the intersection of deep learning, computer vision, robotics, embodied AI, and edge AI, and is delivered in collaboration with Intel, including industrial co-supervision.

The research will explore how robots can interpret instructions, decompose tasks, navigate dynamic environments, manipulate objects, detect failures, and replan over time. Possible directions include long-horizon planning with monitoring and recovery, hybrid reasoning, vision-language task decomposition, world models for prediction and planning, reactive-deliberative architectures, compute-aware evaluation, edge/cloud orchestration, and real-robot testing.

The studentship is supported by Durham’s facilities, including Bede HPC, GPU clusters, LiDAR, RADAR, drones, cameras, embedded devices, and multiple robot platforms. The project is suitable for candidates interested in machine learning, efficient inference, practical deployment, and reliable real-world AI systems.

Funding: Home tuition fees plus a tax-free stipend. This opportunity is available to Home fee-status applicants only.

Eligibility: A relevant undergraduate or master’s degree in computer science, AI, engineering, mathematics, physics, or a related field; strong programming skills; and an interest in ML, computer vision, robotics, embodied AI, or autonomous systems. Experience with robotics, reinforcement learning, foundation models, vision-language models, or efficient inference is desirable but not required.

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 close on 15 August 2026, with interviews expected shortly after and a proposed October 2026 start date.

Funding details

Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.

How to apply

Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.

More information can be found here

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