Post-doctoral Research Associate in Artificial Intelligence (AI) and Machine Learning at Imperial College London

Location: Hammersmith Campus (East Acton) 

Job Summary 

Developing artificial intelligence-enabled electrocardiograms and intracardiac electrograms

Applications are invited for the position of post-doctoral research associate, to work at the National Heart and Lung Institute, Imperial College London. We are a multi-disciplinary team seeking a highly motivated post-doctoral research associate to join a collaborative team of clinicians, basic scientists, and physical scientists working on cardiovascular research, currently funded by a British Heart Foundation Programme Grant. The present post is funded by a UKRI Impact Accelerator Award and an NIHR Biomedical Research Centre grant, both focused on developing new artificial intelligence (AI) models to apply to electrophysiological signals, in the form of electrocardiograms (ECG) and intracardiac electrograms (EGM). 

Through various collaborators worldwide, we have access to large quantities of digital ECGs (>2 million) recorded in clinical settings, in addition to >40000 digital ECGs from the UK Biobank linked to genetic and phenotypic data, which will allow us to train a range of AI-ECG models. We also have access to a large database of intracardiac EGM data collected during invasive catheter ablation procedures, and from collaborators in industry. 

The post-doctoral research associate will work with other members of the team to use these datasets to develop new AI-ECG models and AI-EGM models for cardiovascular risk and mortality prediction, and for diagnostic classification tasks, building on our group’s recent publications:

1. Predicting atrial tachycardia mechanism from the ECG: https://academic.oup.com/ehjdh/article/3/3/405/6668985
2. Predicting body mass index and cardiovascular risk from the ECG: https://www.cvdigitalhealthjournal.com/article/S2666-6936(21)00119-5/fulltext
3. Predicting atrial fibrillation organisation from the ECG: https://www.frontiersin.org/articles/10.3389/fphys.2021.712454/full
4. Applying deep learning methods to digitise paper-based ECGs for AI tasks: https://www.nature.com/articles/s41598-022-25284-1
5. Predicting narrow complex tachycardia mechanism from the ECG: https://www.cvdigitalhealthjournal.com/article/S2666-6936(23)00005-1/fulltext
6. Role of AI-enabled electrograms in healthcare https://bmjmedicine.bmj.com/content/2/1/e000193.info

Specifically, the post-holder will work on two main projects:

1. Developing artificial-intelligence enabled electrograms to prevent unnecessary and harmful defibrillator shocks
2. Extending our work on AI-enabled ECGs above, using the ECG as a 2-dimensional image rather than a 1-dimensional electrophysiology signal

Duties and responsibilities

The post-doctoral research associate will develop new AI-ECG models and AI-EGM models for cardiovascular risk and mortality prediction, and for diagnostic classification tasks. The post holder will work closely with the clinicians, biologists, bioengineers and physical scientists within the multi-disciplinary group.

Essential requirements

You should have a PhD (or submitted a PhD and awaiting viva), or an equivalent qualification, industrial or commercial experience, in a computational discipline. Experience in computer programming (in particular Python), is essential, and previous experience with AI research is highly desirable. A background in cardiovascular research is also desirable. You should also have a collaborative approach to research as this role requires working with other groups both within and outside the department. 

Further Information

Full-time, fixed-term for a duration of 2 years based at the Hammersmith Campus (East Acton), with the possibility of extension following the first 2 years.

*Candidates who have not yet been officially awarded their PhD will be appointed as a Research Assistant within the salary range £40,694 – £43,888 per annum.

Informal enquiries may be sent to Dr Fu Siong Ng (f.ng@imperial.ac.uk)

Closing date: 12/09/2023

To apply, visit www.imperial.ac.uk/jobs and search by the job reference MED04084