Project Overview
This research aims to develop and test machine learning models that can recognise specific imaging planes acquired during the first trimester fetal ultrasound scan. This will be the first step in developing an AI‑powered tool that can be used clinically to assist sonographers in undertaking these scans.
The role will be based at King’s College London, working closely with clinical teams at Guy’s & St Thomas’ NHS Foundation Trust. The project includes collaboration with NHS partners and industry stakeholders to ensure that the reporting interface is technically robust, clinically usable and ready for translation into routine practice.
About the Role
The specific aim of the post will be to train machine learning models to detect and classify specific imaging planes using our pre‑existing dataset of fetal ultrasound scans. A range of machine learning approaches will need to be tested.
* Develop and test machine learning models designed to identify specific image planes corresponding to particular fetal organs, from a stream of ultrasound video data.
* Work independently on defined research packages while consulting with the PI and collaborators as needed.
* Contribute to the planning, design, and management of new and ongoing project components.
* Collaborate effectively with internal and external partners.
* Stay up to date with relevant scientific literature in medical AI.
* Lead and contribute to writing high‑quality scientific publications.
* Present findings at conferences and group meetings.
* Maintain accurate and reproducible records of research activities.
* Support training and supervision of students or junior team members.
* Participate in routine responsibilities such as data handling, code documentation, and ensuring safe working practices in the lab.
* Contribute to teaching activities when requested.
This is a full‑time post (35 hours per week), and you will be offered a fixed‑term contract until 31st May 2027.
Research staff at King’s are entitled to at least 10 days per year (pro‑rata) for professional development. This entitlement 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 look for candidates with the following skills and experience:
Essential Criteria
1. PhD awarded (or pending) including work on medical AI applied to ultrasound
2. Strong background in medical AI model training and testing, with experience of AI applied to fetal ultrasound
3. Ability to work independently
4. Excellent communication skills
5. Strong organisational skills, with the ability to manage tasks independently, meet deadlines and work calmly under pressure.
Desirable Criteria
1. Track record of written publications and presentations at conferences
2. Knowledge of medical imaging, particularly ultrasound, and/or familiarity with DICOM and clinical imaging workflows
3. Experience with standard software engineering practices, including version control systems (e.g. Git), software testing methodologies and collaborative code development.
Equality and Diversity
The Equality Act 2010 protects the rights of our students and staff and provides a framework to fulfil our duties to eliminate unlawful discrimination, harassment and victimisation and to advance equality of opportunity.
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