Automated Solar Cells Crack Detection using AI techniques

The accurate detection and quantification of cracks and defects in structures such as solar cells are essential to allow owners and operators of these high-value assets to understand and quantify their condition. Such arrangements are typically inspected using remote digital cameras to capture images studied by experts and often analysed in detail through a laborious, manually intensive process. This PhD project aims to design a novel image interpretation/classification algorithm to perform automated crack detection and sizing accurately and robustly. This will bring together traditional research approaches to image and signal processing and combine them with state-of-the-art techniques in machine learning, such as deep convolutional neural networks.

For more information about the PhD project, please contact Dr Mahmoud Dhimish: [Email Address Removed]

Entry requirements:

Candidates must have (or expect to obtain) a minimum of a UK upper second-class honours degree (2.1) in an Engineering discipline (Electronics, Electrical, or Energy), Physics, Computer Science or in a related subject

Candidates with prior knowledge or experience in handling any techniques such as photovoltaics (PV) characterisation, machine learning, artificial intelligence, or MATLAB will be desirable, although it is not an essential requirement.

How to apply:

Applicants should apply via the University’s online application system at https://www.york.ac.uk/study/postgraduate-research/apply/. Please read the application guidance first so that you understand the various steps in the application process.