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Zhibao Mian

11 months ago

PhD Studentship: SafeML-based Confidence Generation and Explainability for UAV-based Anomality Detection of Blades Surface in Offshore Wind Turbines University of Hull in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Expired

Country flag

Country

United Kingdom

University

University of Hull

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Keywords

Computer Science
Data Science
Electrical Engineering
Mathematics
Statistical Analysis
Artificial Intelligence
Image Processing
Anomaly Detection
Offshore Wind
Interpretability
Statistics
Confidence Building
Deeplearning
Machinelearning
Maintenance
Drones

About this position

[£20,780per annum]

This fully funded PhD studentship at the University of Hull focuses on developing SafeML-based confidence generation and explainability methods for UAV-based anomaly detection of blade surfaces in offshore wind turbines. The project addresses the growing use of unmanned aerial vehicles (UAVs) for equipment anomaly and fault detection in offshore wind energy, where image quality and decision confidence are critical for reducing maintenance costs and downtime.

The research aims to propose a methodology that generates confidence in decisions made from drone-captured images, using the SafeML tool—a novel open-source safety monitoring tool. The approach involves measuring statistical differences between new images and trusted datasets (validated by experts during model training) to assess confidence in anomaly detection outcomes. This methodology will enhance deep learning explainability and interpretability, providing insights for wind farm owners, system designers, and third-party UAV operators regarding the causes of incorrect diagnoses, algorithmic responsibility, and image quality issues.

Supervised by Dr Zhibao Mian, Dr Koorosh Aslansefat, and Professor Yiannis Papadopoulos, the project offers interdisciplinary training opportunities, including introductory MSc modules in AI and Data Science and a dedicated Safe AI module delivered by the supervisor group. These will equip the candidate with advanced digital and data science research skills, preparing them for careers in data science, safe AI, or further research addressing future technological challenges.

Eligibility requires a First-class Honours degree, or a 2:1 Honours degree and a Masters, or a Distinction at Masters level with any undergraduate degree (or international equivalents) in engineering, computer science, or mathematics and statistics. Non-native English speakers or those requiring a Student Visa must provide evidence of English proficiency (IELTS 7.0 overall, minimum 6.0 in each skill). The studentship provides funding of £20,780 per annum.

Applications should be submitted via the project link. For further information, contact Dr Zhibao Mian at [email protected]. The application deadline is 5 January 2025.

Funding details

Available

What's required

Applicants should have a First-class Honours degree, or a 2:1 Honours degree and a Masters, or a Distinction at Masters level with any undergraduate degree (or the international equivalents) in engineering, computer science, or mathematics and statistics. If your first language is not English, or you require a Student Visa to study, you must provide evidence of English language proficiency meeting the requirements of the academic partners. This course requires academic IELTS 7.0 overall, with no less than 6.0 in each skill.

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Professors

Professor at University of Hull

University Name
Country flag

United Kingdom

Research Interests

Artificial Intelligence

Statistics

Fault Detection

Fault-tolerant Systems

Safety Engineering