About the role:
Catheter ablation therapy involves ‘burning’ small areas of cardiac tissue in order to permanently disrupt the problematic electrical pathways driving potentially lethal arrhythmias. However, procedure times and complication rates are high, whilst success rates are punitively low (~50% success), largely due to the significant challenges clinicians face in identifying the ideal ‘target’ to ablate. Here, we aim to further develop, clinically validate, and prospectively evaluate, a novel in-silico tool that uses patient imaging data to reconstruct personalised ‘digital twin’ cardiac models to provide pre-procedural ablation target guidance. This will provide essential confidence and data to guide a follow-up prospective randomised clinical trial. In this project, funded by a British Heart Foundation Programme Grant, we will refine our existing research computational tool and technology into a robust, well-engineering software as a medical device, ready for clinical evaluation. The tool will then be deployed in a clinical study to evaluate its performance in enhancing patient therapy.
This is a full time post (35 Hours per week), and you will be offered a fixed term contract for up to 2 years (grant dates are 01.04.26 – 30.06.31 so flexible within this).
Research staff at King’s are entitled to at least 10 days per year (pro-rata) for professional development. This entitlement, from the Concordat to Support the Career Development of Researchers, applies to Postdocs, Research Assistants, Research and Teaching Technicians, Teaching Fellows and AEP equivalent up to and including grade 7. Visit the Centre for Research Staff Development for more information.
About you:
To be successful in this role, we are looking for candidates to have the following skills and experience:
Essential criteria
1st or 2nd class hons degree in computer science, biomedical engineering, physics, mathematics or related subject
PhD qualified in computer science, biomedical engineering, physics, mathematics or related subject
Excellent programming skills in Python, including familiarity with libraries such as NumPy, SciPy and Pandas
Familiarity with Linux/ Unix-based systems
Good writing and presentation skills
Desire to work in medical modelling in a clinical environment
Able to work on own initiative and in a team, and communicate effectively with people from wide variety of disciplines and organisations
Flexible approach to hours of work and duties
Desirable criteria
Experience in cardiac research
Knowledge of cardiac physiology
Knowledge of how to perform numerical simulations and analysis
Advanced knowledge of signal and image processing techniques
Formal experience of software engineering