Applications are currently open for this fully funded project. The proposed PhD will address several primary research areas, with main objectives:
- Investigating how LLMs can be trained and deployed as NDE assistants, capable of interpreting ultrasonic testing data. This research will explore the use of both text and image prompts to guide LLMs in offering suggestions and making informed decisions on the interpretation of various incoming UT data.
- Assessment of Multi-Modal LLMs for Localised Deployment: A comprehensive review of the state-of-the-art in multi-modal LLMs is required to identify models and techniques suitable for localized deployment in NDE applications.
- Exploring how LLMs can be utilised to generate quality reports that adhere to relevant standards and guidelines. These reports will be designed for use in various downstream manufacturing tasks, while bearing in mind the compliance with industry requirements.
- LLM-Guided Inspection Parameter Optimisation and Defect Detection: Conducting extensive testing to evaluate how LLMs can assist in setting up and refining inspection parameters. The research will assess their ability to dynamically adjust parameters based on textual and visual data, handle anomalies, and guide users through the inspection process.
- Defect detection and analysis capabilities of LLMs should be benchmarked against traditional and other AI-based methods, with a specific focus on zero-shot and few-shot learning approaches. Further investigation will assess whether additional training in defect detection is necessary to enhance model performance.
Table of Content
Summary
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Benefits
Funding is provided for full tuition fees (Home/EU applicants only). The student will receive the standard tax-free EPSRC stipend rate of £20,780/annum) and equipment and travel funds for the duration of the project.
Requirements
The applicant should meet the EPSRC studentship eligibility criteria:
- Possess an Upper second (2.1) UK BEng Honours or MEng degree in relevant engineering disciplines (Electrical, Mechanical, Naval, Design and Manufacturing, etc.) or physics-related subjects
- Be a UK or an eligible EU national and adhere to EPSRC eligibility criteria.
Candidates with the Knowledge and experience of:
- background/knowledge in Machine Learning and Deep Learning, and the relevant Python/MATLAB libraries
- Physics of Ultrasound, and other NDE techniques such as electromagnetic testing
- programming and coding platforms such as Python, MATLAB, and C
are desirable.
The subjects that would be considered for the position:
- Electronic and Electrical Engineering (EEE)
- Physics
- Mechanical Engineering
- Naval
- Design Manufacturing and Engineering Management (DMEM)
Application Documents
- CV
- Academic transcript
- Cover letter
Check also:
Wits-Edinburgh Programme in Sustainable African Futures by Mastercard Foundation 2026-2027
Department of Aeronautics MSc Scholarship at Imperial College London 2026
Application Deadline
November 30, 2025How To Apply
For more details, visit University of Strathclyde Scholarship webpage
