Research Associate in Calibrating Cardiac Digital Twins at Scale
Artificial intelligence (AI), machine learning (ML) and Digital Twins (DT) have the potential to transform cardiology. We are seeking to appoint a data scientist/engineer to develop and apply state of the art AI methods to benefit patient outcomes at St Thomas’ Hospital and other major London centres for cardiovascular disease. This project will develop and apply state of the art machine learning methods and physics-based models to analyse longitudinal patient data and encode this information within a digital twin of the patient’s heart, in order to provide doctors with detailed information on the trajectory of heart disease.
The role will require the development, application and refinement of tools to 1) interpret MRI data to measure patient cardiac anatomy, motion and structure, 2) calibration of cohorts of patient models to clinical data, 3) analyse the data, and 4) provide reports and derived data on the anatomy and function of the heart for evaluating and identifying patient disease trajectories. This will include analysis of clinical data and medical images to provide geometric models of the heart, as well as analysis of scar and tissue characteristics. These data will be used to develop digital twins of patient hearts to predict trajectories and outcomes in relation to disease status, treatments, and interventions.
Although AI and ML tools have been developed for image analysis and other applications, application to clinical data and workflow requires robust integration and ability to work with a wide variety of cardiovascular diseases. We wish to move beyond conventional analysis of images taken from a single hospital visit but analyse longitudinal data recorded over months or years. This is a critical step in developing digital twins. This project will determine how current tools can be developed and extended to work with clinical workflows and longitudinal data for the benefit of patients.
The role will require the use of databases and development of data analysis methodologies, including regression and classification techniques. Simulations will be performed using the cardiac focused CARP software on local and nation high performance computing resources.
This role requires excellent software development skills, including requirement analysis, design, development, testing, and maintenance of different software components. In addition, the role provides the opportunity for research that develops novel methodologies for machine learning and computational statistics methods in cardiovascular disease.
The role will be based at King’s College London, in collaboration with clinical teams at St Thomas’ Hospital and will directly with members of the cardiology and radiology research team.
About the Faculty
About the Department of Biomedical Engineering
About the Cardiac ElectroMechanics Research Group
This post will be offered on a fixed-term contract for 2 years
This is a full-time post
The successful applicant will be responsible for developing and integrating tools for clinical and imaging data analysis and reporting, including imaging and statistical modelling for analysing outcomes in relation to imaging biomarkers.
The applicant should ideally have some knowledge and experience of:
1. Physics-based modelling and simulation
2. Inverse problems
3. Statistical modelling
4. Machine learning
The position would appeal to a candidate with strong software development skills, including:
1. Machine learning packages (Tensorflow, PyTorch,…)
2. Finite element method
3. Statistical modelling packages (R, Stata, …)
• Experience using cardiac simulation software is an advantage but not essential. Experience using software libraries for ML is an advantage but not essential. An interest in cardiac medical modelling and simulation is important, but previous experience is not essential. Previous experience with cloud computing services would be useful but not essential.
• Strong communication skills are required to work with researchers from other disciplines, such as clinical end-users, and industry collaborators.
• The candidate is also expected to:
• work in close collaboration with the other members of the research group
• work towards the groups open-source software stack
• Experience working with interdisciplinary teams of engineers and clinicians will be valued.
• A strongly independent applicant is required who will need to work well with inter-disciplinary teams.
The above list of responsibilities may not be exhaustive, and the post holder will be required to undertake such tasks and responsibilities as may reasonably be expected within the scope and grading of the post.
Skills, knowledge, and experience
1. PhD awarded in Mathematics, Engineering or Computer Science or PhD in Mathematics, Engineering or Computer Science near completion.
2. Undergraduate or higher degree in engineering, applied maths or computer science
3. Higher language computer programming
4. Scientific / Medical Writing
5. Interest in medical imaging
6. Ability to work calmly under pressure
7. Ability to act on initiative
1. Cardiac modelling and simulation
2. Inverse problems / model personalization
3. General machine learning experience
6. Data analysis packages (R, SAS,…)
7. Machine learning libraries (Tensorflow, pytorch,…)
8. Knowledge of software development cycles
9. Cloud computing services (AWS, Docker, Containers)
10. Independent and interdisciplinary researcher
* 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.
This post is subject to Disclosure and Barring Service clearance.