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About us

The successful applicant will be located with the newly created Research Department of Digital Twins (North Wing of St Thomas鈥� Hospital), primarily supervised by Dr Martin Bishop. The post will benefit from the computational and clinical electrophysiological infrastructure provided by the School of Biomedical Engineering & Imaging Sciences and will be based at St Thomas鈥� Hospital.

This work will be performed in close collaboration with the clinical researchers at KCL and St Thomas鈥� Hospital, lead by Dr John Whitaker and Prof Aldo Rinaldi. The project is also in direct collaboration with Prof Gernot Plank (Medical University of Graz) and the cardiac software and solutions company NumeriCor working with Dr Aurel Neic.

About the role

Catheter ablation therapy involves 鈥榖urning鈥� 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 鈥榯arget鈥� to ablate. Here, we aim to further develop, and clinically validate, a novel in-silico tool that uses patient imaging data to reconstruct personalised 鈥榙igital twin鈥� 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 an internal MRC Impact Acceleration Award, we will use currently-collected patient imaging data to further develop computational pipelines to reconstruct personalised image-based computer models of a patient鈥檚 heart. Imaging data, from modalities such as Cardiac MRI and Cardiac CT will be combined in order to best represent the patient鈥檚 cardiac anatomy, as well as the underlying fibrosis and scar tissue which are responsible for driving the lethal ventricular arrhythmias to be treated.聽 These patient models will then be used within our novel in-silico approach (patent filed) to rapidly simulate patient arrhythmias in near-real time. A crucial aspect of this work will be the further adaptation of this computational approach in order to automatically account for the inherent uncertainty in the clinical data, for example by considering different representations of the scar or of the functional parameters that go into the model. Finally, clinical electrical data from the cath-lab during patient procedures, in the form of both ECG and invasive electro-anatomical mapping data, will then be used to validate the arrhythmia 鈥榗ircuits鈥� predicted by the model.

This is a full time post (35 Hours per week), and you will be offered a fixed term contract up to 1 year.

About you

To be successful in this role, we are looking for candidates to have the following skills and experience:

Essential criteria

1. 1st or 2nd class hons degree in computer science, biomedical engineering, physics, mathematics or related subject 聽
2. PhD qualified in computer science, biomedical engineering, physics, mathematics or related subject (or pending results)*聽
3. Good programming skills in Python, Matlab and/or C++聽
4. Experience in computational biophysical modelling and simulation聽
5. Good writing and presentation skills聽
6. Desire to work in medical modelling in a clinical environment聽
7. Flexible approach to hours of work and duties聽
8. Able to work on own initiative and in a team, and communicate effectively with people from wide variety of disciplines and organisations

* Please note that this is a PhD level role but candidates who have submitted their thesis and are awaiting award of their PhDs will be considered. In these circumstances the appointment will be made at Grade 5, spine point 30 with the title of Research Assistant. Upon confirmation of the award of the PhD, the job title will become Research Associate and the salary will increase to Grade 6.

Desirable criteria

1. Knowledge of cardiac physiology聽
2. Knowledge of how to perform numerical simulations and analysis聽
3. Advanced knowledge of signal and image processing techniques聽
4. Experience in cardiac research

Downloading a copy of our Job Description

Full details of the role and the skills, knowledge and experience required can be found in the Job Description document, provided at the bottom of the next page after you click 鈥淎pply Now鈥�. This document will provide information of what criteria will be assessed at each stage of the recruitment process.

Further information

We pride ourselves on being inclusive and welcoming. We embrace diversity and want everyone to feel that they belong and are connected to others in our community.

We are committed to working with our staff and unions on these and other issues, to continue to support our people and to develop a diverse and inclusive culture at King's. We ask all candidates to submit a copy of their CV, and a supporting statement, detailing how they meet the essential criteria listed in the advert. If we receive a strong field of candidates, we may use the desirable criteria to choose our final shortlist, so please include your evidence against these where possible.

To find out how our managers will review your application, please take a look at our 鈥樷�� pages.

We are able to offer sponsorship for candidates who do not currently possess the right to work in the UK.