Job description
Cardiac malformations are the most common type of birth defect. Improvements in the management of complex congenital heart disease have resulted in >90% of those born with congenital heart disease now able to survive into early adulthood. In particular, patients with tetralogy of Fallot often require replacement of the pulmonary valve.  
 
This role will expand the Cardiac Atlas Project (www.cardiacatlas.org) to a large cohort of patients with tetralogy of Fallot, and to use cardiac magnetic resonance (CMR) exams and other clinical data to derive statistical atlases of shape, biomechanics and electrical dyssynchrony. These atlases will be used to test hypotheses, such as the optimal time to replace the pulmonary valve, and discover clinical biomarkers that predict outcomes of pulmonary valve replacement based on variations in ventricular shape, mechanical properties and electromechanical dyssynchrony. Machine learning methods will be used to generate and analyse statistical shape models.
 
The role will be based at King’s College London, in collaboration with clinical teams at St Thomas’ Hospital, and the University of California San Diego as part of a project funded by the National Institutes of Health, USA. 
 
This post will be offered on a fixed-term contract until 30 June 2024. 
This is a full-time post.

Key responsibilities
The successful applicant will be responsible for developing and integrating tools for clinical and imaging data analysis, including statistical modelling for analysing outcomes in relation to imaging biomarkers.  
 
The applicant should ideally have some knowledge and experience of:
 
1.            Medical image analysis
2.            Data science
3.            Statistical modelling
4.            Scientific/engineering programming
 
The position would appeal to a candidate with strong software development skills, including:
 
1.            Machine learning (pytorch, …)
2.            Numerical methods (optimization, nonlinear equations, finite element analysis,…)
3.            Statistical modelling packages (R, Stata, …)  
 
An interest in cardiac mechanics will be useful, but previous experience is 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 clinical and engineering staff 
-          contribute to the Atlas Project web services, database and analysis tools 
 
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 
 
Essential criteria 
 
1.       PhD awarded or 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 
 
Desirable criteria 
 
1.       Knowledge of medical image analysis 
2.       Machine learning 
3.       Python 
4.       Experience in Data analysis packages (R, SAS,…) 
5.       Experience in Cardiac function analysis  
6.       Numerical methods  
7.       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.