[£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.

[£20,780 per annum stipend.] The University of Hull is offering a fully funded PhD studentship focused on SafeML-based confidence generation and explainability for UAV-based anomaly detection of blade surfaces in offshore wind turbines. This interdisciplinary project addresses the growing need for reliable and interpretable AI systems in the maintenance and operation of renewable energy infrastructure. Unmanned Aerial Vehicles (UAVs), such as drones, are increasingly deployed to capture images for equipment anomaly and fault detection in offshore wind turbines. However, image quality can vary due to environmental and operational factors, impacting the accuracy of automated diagnostic decisions. The core aim of this research is to develop a methodology that generates confidence in AI-driven decisions, potentially reducing maintenance costs and minimizing downtime in offshore wind energy production. The project leverages the SafeML tool, an open-source safety monitoring platform, to statistically compare new drone-captured images with trusted datasets—those used to train and validate deep learning models. This approach not only enhances confidence in automated diagnoses but also provides deep learning explainability and interpretability, addressing key questions for wind farm owners, AI system designers, and third-party drone operators regarding decision errors and their origins. Through advanced image pre-processing and deep learning techniques, the research will enable the system to identify and explain issues such as blade erosion or fatigue, ensuring that maintenance interventions are both necessary and cost-effective. The statistical analysis framework developed will clarify why incorrect decisions occur, which components of the AI system are responsible, and whether image quality or pre-processing contributed to errors. As part of the studentship, candidates will have the opportunity to attend introductory MSc modules in AI and Data Science, as well as a specialized Safe AI module delivered by the supervisor team. This training will equip students with cutting-edge digital and data science research skills, preparing them for careers in safe AI, data science, and future technological challenges in renewable energy and beyond. Eligibility requirements include 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, mathematics, or statistics. Applicants whose first language is not English, or who require a Student Visa, must provide evidence of English language proficiency, specifically an academic IELTS score of 7.0 overall with no less than 6.0 in each skill. The studentship provides a generous stipend of £20,780 per annum. Interested candidates should review the full project details and submit their application via the provided link. For further enquiries, contact Dr Zhibao Mian at [email protected]. The application deadline is November 30, 2026.