At a Glance
- Tasks: Develop and test AI models for fetal ultrasound imaging to assist clinical teams.
- Company: Join King’s College London, collaborating with NHS and industry leaders.
- Benefits: Fixed-term contract, professional development days, and a supportive research environment.
- Other info: Opportunity to publish research and present at conferences.
- Why this job: Make a real impact in medical AI and improve healthcare outcomes.
- Qualifications: PhD in medical AI, strong communication, and organisational skills required.
The predicted salary is between 37000 - 44000 € per year.
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.
About You: To be successful in this role, we look for candidates with the following skills and experience:
Essential Criteria:- PhD awarded (or pending) including work on medical AI applied to ultrasound.
- Strong background in medical AI model training and testing, with experience of AI applied to fetal ultrasound.
- Ability to work independently.
- Excellent communication skills.
- Strong organisational skills, with the ability to manage tasks independently, meet deadlines and work calmly under pressure.
- Track record of written publications and presentations at conferences.
- Knowledge of medical imaging, particularly ultrasound, and/or familiarity with DICOM and clinical imaging workflows.
- 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.
Research Associate in Medical Artificial Intelligence in London employer: King’s College London
At King’s College London, we pride ourselves on being an exceptional employer that fosters a collaborative and innovative work culture. As a Research Associate in Medical Artificial Intelligence, you will have access to extensive professional development opportunities, including dedicated time for training and the chance to contribute to impactful research alongside leading clinical teams at Guy’s & St Thomas’ NHS Foundation Trust. Our commitment to equality and diversity ensures a supportive environment where your contributions are valued, making this role not just a job, but a meaningful career path in advancing medical technology.
StudySmarter Expert Advice🤫
We think this is how you could land Research Associate in Medical Artificial Intelligence in London
✨Tip Number 1
Network like a pro! Reach out to professionals in the medical AI field, especially those connected to King’s College London or Guy’s & St Thomas’ NHS Foundation Trust. Attend relevant conferences and workshops to make connections that could lead to job opportunities.
✨Tip Number 2
Show off your skills! Prepare a portfolio showcasing your previous work in medical AI, particularly any projects related to ultrasound. This will give potential employers a clear idea of what you can bring to the table.
✨Tip Number 3
Practice makes perfect! Get ready for interviews by rehearsing answers to common questions in the research field. Focus on your experience with machine learning models and how you've applied them in real-world scenarios.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of resources to help you land that dream job. Plus, it shows you’re genuinely interested in being part of our team at StudySmarter.
We think you need these skills to ace Research Associate in Medical Artificial Intelligence in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the role of Research Associate in Medical AI. Highlight your experience with machine learning models, especially in medical imaging and ultrasound. We want to see how your skills match what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about medical AI and how your background makes you a perfect fit for our team. Don’t forget to mention any relevant projects or publications!
Showcase Your Communication Skills:Since communication is key in this role, make sure your application reflects your ability to convey complex ideas clearly. Whether it's through your CV, cover letter, or any additional documents, we want to see that you can communicate effectively with both technical and non-technical audiences.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the easiest way for us to keep track of your application and ensures you don’t miss out on any important updates. Plus, it shows you’re keen to join our team at StudySmarter!
How to prepare for a job interview at King’s College London
✨Know Your Stuff
Make sure you brush up on your knowledge of medical AI, especially as it relates to ultrasound. Familiarise yourself with the latest research and developments in the field, particularly around machine learning models for fetal imaging. This will not only help you answer technical questions but also show your genuine interest in the role.
✨Showcase Your Experience
Prepare to discuss your previous work in medical AI and any relevant projects you've been involved in. Be ready to explain your role in training and testing models, and how you’ve contributed to publications or presentations. Use specific examples to illustrate your skills and achievements.
✨Collaboration is Key
Since this role involves working closely with clinical teams and external partners, be prepared to talk about your experience in collaborative environments. Highlight any instances where you successfully worked with others to achieve a common goal, and demonstrate your communication skills.
✨Stay Organised
The ability to manage tasks independently and meet deadlines is crucial. Before the interview, think of ways you've effectively organised your work in the past. You might want to share strategies you use for keeping track of research activities, data handling, and documentation, as these are important aspects of the role.
