King's College London

About Us

The Research Department of Digital Twins for Healthcare at King’s College London is a multidisciplinary hub within the School of Biomedical Engineering & Imaging Sciences, dedicated to advancing precision medicine through computational replicas of human physiology and healthcare systems. The department brings together expertise in engineering, medical imaging, computational modelling, and clinical sciences to develop digital technologies capable of transforming the diagnosis, treatment, and monitoring of complex diseases.

Located at St Thomas’ Hospital, one of the UK’s leading research and teaching hospitals, the department works closely with clinicians and other research groups to ensure strong translational impact. Its mission is to revolutionise healthcare through data‑driven modelling, machine learning, multiscale and multiphysics simulation, computational anatomy, medical image analysis, and integration of wearables and biosignal processing, applied to conditions ranging from cardiac arrhythmias to blood disorders and cardiomyopathies. [kcl.ac.uk]

The department places strong emphasis on collaboration, fostering partnerships across academia, clinical practice, industry, and patient communities to ensure that innovations are both scientifically rigorous and clinically useful. It also connects with major training programmes such as the Centre for Doctoral Training in Digital Twins for Healthcare (DT4Health), which prepares future leaders to advance digital‑twin research in real‑world healthcare applications. [kcl.ac.uk]

Overall, the Department of Digital Twins for Healthcare serves as a leading international centre for developing computational models that support personalised, efficient, and predictive healthcare—advancing the future of medicine through digital transformation.

More info:

 https://www.kcl.ac.uk/bmeis/our-departments/digital-twins-for-healthcare

 https://www.kcl.ac.uk/research/dt4health-cdt

About The Role

We are seeking a highly motivated postdoctoral researcher to lead the development of an advanced AI pipeline for fetal cardiovascular imaging, with the goal of improving early diagnosis of coarctation of the aorta (CoA). The purpose of this role is to create a clinically impactful system capable of reconstructing the 3D fetal aortic arch from routine 2D ultrasound views by combining generative modelling, deep learning, and rigorous clinical validation. Working within a multidisciplinary team spanning King’s College London and BCNatal (Barcelona), the researcher will contribute to a project that sits at the intersection of computational imaging, cardiovascular medicine, and translational healthcare innovation.

 

The researcher will be responsible for designing and implementing diffusion‑based generative models to produce high‑quality synthetic ultrasound views derived from 3D anatomical models. These datasets will serve as the foundation for training robust 3D reconstruction networks, building on architectures such as Pix2Vox++, to infer the fetal aorta and ductus arteriosus from multiple ultrasound planes. A key part of the role will be ensuring model robustness to clinically realistic challenges including 

imaging noise and variability in probe orientation.

 

Beyond model development, the researcher will evaluate reconstruction accuracy using established 2D diagnostic metrics, expert fetal cardiologist assessments, and uniquely matched MRI–ultrasound datasets. They will also assess the pipeline’s diagnostic utility by predicting CoA risk in a large retrospective cohort, comparing performance against current clinical practice. Throughout the project, the researcher will collaborate closely with engineers and clinicians, iterating through rapid development cycles to produce a validated, reproducible, and clinically relevant diagnostic prototype.

 

This role offers the opportunity to drive high‑impact innovation in fetal cardiovascular imaging and contribute to improving outcomes for babies with congenital heart disease.

 

This is a full-time post (35 hours per week), and you will be offered a fixed term contract for two years.

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

1. PhD qualified in relevant subject area (Computer Science, Biomedical Engineering, Medical Imaging, or a related field, with a strong focus on machine learning or computational imaging.) – at or near completion.
2. Demonstrated expertise in deep learning (e.g., CNNs, generative models, 3D reconstruction architectures), evidenced by high quality publications, code repositories, or project outputs
3. Advanced programming skills, particularly in Python and deep learning frameworks such as PyTorch or TensorFlow, with proficiency in managing complex model training pipelines.
4. Strong analytical and problem-solving skills, with the ability to design experiments, evaluate model performance, and interpret quantitative results.
5. Excellent communication skills, including the ability to work effectively with clinicians and translate engineering developments into clinically meaningful outputs.
6. Track record of successful interdisciplinary collaboration, especially with clinical or healthcare partners.
7. Curiosity and independence, with the ability to drive forward complex technical developments with minimal supervision.
8. Commitment to translational impact, motivated by the opportunity to improve diagnosis of congenital heart disease.

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. Experience with generative modelling, ideally including diffusion models or GANs, and their application to medical or scientific image synthesis.
2. Strong background in 3D geometry, shape modelling, or voxel /mesh based representations, including methods for shape inference or reconstruction.
3. Experience working with medical imaging data, such as ultrasound or MRI, including an understanding of common sources of noise, variability, and bias.
4. Experience with statistical shape models, particularly in a clinical or anatomical context.
5. Background in ultrasound image analysis, including familiarity with 2D fetal cardiac views or probe pose estimation.

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 page. 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.

As part of this commitment to equality, diversity and inclusion and through this appointment process, it is our aim to develop candidate pools that include applicants from all backgrounds and communities.  

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 person specification section of the job description. 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 ‘How we Recruit’ pages.