PhD in Explainable and Efficient AI-Enabled EEG Systems Aston University in United Kingdom

This fully funded PhD position at Aston University offers an exciting opportunity to advance the field of AI-enabled EEG systems, focusing on explainability and efficiency in clinical applications. Hosted by the Aston Centre for Artificial Intelligence Research and Application within the College of Engineering and Physical Sciences, the project is supported by EPSRC and the College of Engineering and Physical Sciences (EPS).

The research aims to address two major challenges in clinical AI: the explainability of deep learning models and the efficiency of data annotation. You will develop and evaluate explainable AI (XAI) strategies to generate human-readable EEG reports, translating complex brain signals into clinically meaningful insights. The project leverages state-of-the-art deep learning architectures, including transformers and convolutional neural networks, as well as large language models (LLMs) such as GPT-style models. You will work with real-world EEG data and explore event localization networks to enhance the interpretability of AI-driven EEG analysis.

In parallel, the project investigates innovative methods to improve label efficiency in deep learning, such as integrating eye-tracking technologies and multimodal data inputs (e.g., voice). These approaches aim to reduce manual annotation time while maintaining diagnostic accuracy, with the potential to transform automated teleneurology systems. The interdisciplinary nature of the project provides hands-on experience with advanced AI tools, deep learning frameworks, and neuroinformatics methods, preparing you for careers in both industrial AI labs and academic research.

Applicants should have, or expect to achieve, a First Class or 2.1 Bachelor's degree in a relevant subject, or a First Class or 2.1 Bachelor's degree plus a Master's degree with Merit or higher. International equivalents are accepted. Desirable skills include a strong background in Computer Science, Artificial Intelligence, Biomedical Engineering, or related fields; programming experience in Python or MATLAB; familiarity with deep learning frameworks (PyTorch, TensorFlow); prior experience or interest in EEG signal processing, biomedical data analysis, or neuroinformatics; knowledge of machine learning and deep learning techniques (CNNs, Transformers, generative models); understanding of explainable AI for healthcare; experience with eye-tracking or multimodal data acquisition; ability to work collaboratively in interdisciplinary teams; and strong scientific writing and communication skills. English language proficiency is required.

The studentship covers full tuition fees and provides a standard stipend in line with UKRI rates. The position is based at the Aston Campus in Birmingham, UK, and is open to both UK and overseas applicants. Regular in-person attendance is expected. Interviews will be conducted online via Microsoft Teams for shortlisted candidates.

To apply, submit a complete application including transcripts, certificates, a research statement, personal statement, CV, two academic references, evidence of English language proficiency, and a copy of your passport. Incomplete applications will not be considered. For further information, contact Dr Ziyang Wang at [email protected] or the Postgraduate Admissions team at [email protected]. For full details and to apply, visit the project page.