We are seeking motivated and self-funded PhD students who are excited to work at the intersection of machine learning, artificial intelligence, and real-world impact. Our research spans several applied domains, including security and healthcare. Example topics include person identification using gait-based approaches, AI for clinical assessment, and applications in areas such as ophthalmology and mobility analysis.

Alongside these applications, we have strong interests in core methodological questions in explainable AI (XAI) and uncertainty. Projects may involve developing interpretable models for high-risk settings, investigating robust uncertainty estimation, or exploring trustworthy AI systems that can operate reliably in complex environments.

Students will have the opportunity to shape their own research directions within these themes. Applicants should have a background in computer science, engineering, mathematics, or a related field and should be comfortable with programming and quantitative analysis. Experience in machine learning is helpful but not essential for highly motivated candidates.

If you are interested in pursuing a PhD in a supportive environment where you can explore ambitious ideas in AI, please get in touch with an outline of your interests, your CV, and any relevant project or publication links. Potential candidates will be encouraged to discuss project ideas and possible application routes, including self-funded or scholarship options where available.

For queries, please contact Dr Luke Topham (l.k.topham@ljmu.ac.uk).

We are seeking motivated and self-funded PhD students who are excited to work at the intersection of machine learning, artificial intelligence, and real-world impact. Our research spans several applied domains, including security and healthcare. Example topics include person identification using gait-based approaches, AI for clinical assessment, and applications in areas such as ophthalmology and mobility analysis.

Alongside these applications, we have strong interests in core methodological questions in explainable AI (XAI) and uncertainty. Projects may involve developing interpretable models for high-risk settings, investigating robust uncertainty estimation, or exploring trustworthy AI systems that can operate reliably in complex environments.

Students will have the opportunity to shape their own research directions within these themes. Applicants should have a background in computer science, engineering, mathematics, or a related field and should be comfortable with programming and quantitative analysis. Experience in machine learning is helpful but not essential for highly motivated candidates.

If you are interested in pursuing a PhD in a supportive environment where you can explore ambitious ideas in AI, please get in touch with an outline of your interests, your CV, and any relevant project or publication links. Potential candidates will be encouraged to discuss project ideas and possible application routes, including self-funded or scholarship options where available.

For queries, please contact Dr Luke Topham (l.k.topham@ljmu.ac.uk).