Cardiovascular diseases are the leading cause of death globally, accounting for 17.9 million deaths annually (WHO). Early detection of arrhythmias can save lives, yet it remains challenging due to signal variability. This project addresses the critical gap between analytical methods and AI-driven solutions for analysing biomedical signals such as ECG. By combining these approaches, the project aims to develop interpretable AI tools, ensuring safer and more reliable decision-making—an essential factor in healthcare applications.

The successful applicant will gain advanced expertise in exponential analysis, inverse problems, AI/ML techniques, signal processing, and mathematical modelling, along with invaluable industry collaboration experience (several months) with international partners, equipping them with skills highly sought after in academia and industry.

Objectives:

Develop a hybrid methodology that integrates classical analytical methods and AI/ML tools to improve the classification of biomedical signals, such as identifying ECG patterns and detecting the onset of arrhythmias.

Ensure AI decisions are interpretable and safe, addressing the critical need for transparency in healthcare applications.

Leverage industry collaboration with industry collaborators to validate and implement algorithms in virtual models and medical devices.

Achieve integration of these tools into real-world healthcare systems, bridging the gap between research and practical application.

Research Question : How can the integration of analytical modelling and AI/ML techniques enhance the reliability and safety of biomedical signal classification, such as ECG or EEG, and how can these methods be validated and implemented for real-world medical applications?

Candidate Prerequisites :

A strong academic background in applied mathematics, computer science, or biomedical engineering and a Master’s degree (MSc) in a relevant field is essential.

Proficiency in programming languages such as Python is essential; experience with MATLAB is a plus.

Familiarity with signal processing techniques and tools is beneficial.

Experience with AI/ML frameworks such as TensorFlow or PyTorch is beneficial.

How to apply

Closing date for applications is 24 ^th March 2025 .

This 3-year FTE PhD position is funded by the University of Stirling and Virtonomy . The expected start date is 1 ^st October 2025.

Applicants should submit an Expression of Interest here ( https://forms.office.com/e/hdS67Nh8gd ). Further information on the IAS Studentships 2025 competition, including a guidance document and FAQ are available here: https://www.stir.ac.uk/research/research-degrees/institute-for-advanced-studies-studentships/

For informal enquiries please contact Anya Kirpichnikova ( [email protected] )