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University of Surrey

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PhD in Experimental Materials Characterisation and Machine Learning for Materials Performance University of Surrey in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

PhD opportunity through a collaborative University of Surrey and National Physical Laboratory project. The post indicates research training and access to complementary academic expertise, NPL measurement science, facilities, and industrial links, but does not state stipend, tuition, or fee details in the post text.

Deadline

Oct 9, 2026

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Country

United Kingdom

University

University of Surrey

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Keywords

Computer Science
Mechanical Engineering
Materials Science
Measurement Science
Material Characterization
Digital Transformation
Physics
ML

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About this position

PhD opportunity at the University of Surrey in collaboration with the National Physical Laboratory (NPL) in the United Kingdom.

This project focuses on experimental materials characterisation, machine learning, materials performance evaluation, structural integrity, and the digital transformation of materials research. The successful PhD researcher will work across Surrey and NPL, gaining access to complementary academic expertise, world-leading measurement science, advanced facilities, and industrial links.

The post is especially relevant for students interested in materials science, computer science, and engineering applications of data-driven methods. The collaboration highlights a strong Surrey–NPL partnership and offers a research environment that combines experimental work with computational analysis.

Location: Teddington, United Kingdom (NPL) with collaboration across Surrey and NPL.

Deadline: 9 October 2026.

How to apply: Follow the application link in the post to the NPL Careers page and submit your application through the portal. Informal enquiries can be directed to the poster and Tony Fry.

Funding details

PhD opportunity through a collaborative University of Surrey and National Physical Laboratory project. The post indicates research training and access to complementary academic expertise, NPL measurement science, facilities, and industrial links, but does not state stipend, tuition, or fee details in the post text.

What's required

Applicants should be interested in a PhD research project combining advanced experimental materials characterisation with machine learning. The post implies the candidate will work across academic and national laboratory environments; no specific degree class, language test, or technical prerequisite is stated in the post.

How to apply

Use the full details and application link provided in the post to access the NPL Careers page. Submit the application through the linked portal before 9 October 2026. For informal enquiries, contact the poster and Tony Fry.

More information can be found here

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