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University of Sussex

PhD Studentship: AI-Driven Ultrasound for Materials Evaluation (2026) University of Sussex in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Full funding available

Deadline

Sep 11, 2026

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Country

United Kingdom

University

University of Sussex

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Keywords

Computer Science
Mechanical Engineering
Electrical Engineering
Materials Science
Deep Learning
Mathematics
Artificial Intelligence
Non-destructive Testing
Uncertainty Analysis
Wave Mechanics
Computational Mechanics
Artificial Neural Network
Statistics
Inverse Problem
Physics
Ultrasonic

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About this position

[Fully funded for 3.5 years: tax-free stipend of £21,805 per year, tuition fees waived at UK or international rate, plus a one-off Research and Training Support Grant of £2,000.]

This is a fully funded PhD studentship at the University of Sussex focused on AI-driven ultrasound for materials evaluation. The project sits at the intersection of physics, engineering, materials science, and machine learning, and aims to develop quantitative inverse models that extract material properties or defect information from ultrasonic measurements.

Ultrasound is widely used for non-destructive evaluation in safety-critical settings such as aircraft components, pipelines, and electric vehicle batteries. However, interpreting ultrasonic signals is difficult because wave propagation in real materials is complex. This PhD tackles that challenge by combining large-scale simulation, deep learning, uncertainty quantification, and experimental validation in the Sussex ultrasonic laboratory.

You will work through the full research pipeline: generating training data with ultrasound simulations, designing and benchmarking neural network architectures, estimating prediction uncertainty, and testing models against experimental measurements. The project emphasizes bridging the simulation-to-experiment gap and developing methods that can be applied to metals and layered structures.

The studentship offers access to advanced ultrasonic instrumentation, high-performance computing, and a dynamic research environment with academic and industrial links. It is well suited to students interested in applying AI to real-world physics and engineering problems, especially non-destructive testing and inverse modelling.

Funding is strong and fully covers the 3.5-year PhD period: a tax-free stipend of £21,805 per year, tuition fees at the UK or international rate, and a £2,000 Research and Training Support Grant. The opportunity is open to UK and international applicants.

Applicants should have, or expect to obtain, a strong undergraduate or Master's degree in physics, engineering, applied mathematics, materials science, computer science, or a related subject. Useful but non-essential experience includes ultrasound or wave physics, numerical simulation, Python, and machine learning frameworks.

The deadline is 11 September 2026. Interested candidates should apply through the University of Sussex funding page and may contact Dr Ming Huang or Dr Ivor Simpson for informal enquiries.

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.

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