Senior AI Engineer on Real-world Healthcare Data

About us

The School of Biomedical Engineering & Imaging Sciences (BMEIS), part of King’s College London, is driven by a commitment to improve the way healthcare is delivered through the use of advanced engineering. 

BMEIS is diverse and talented group working across the whole MedTech sector, advancing research, innovation and teaching progress through a shared mission of engineering better health for patients worldwide. The School’s state-of-the-art labs and clinical-research facilities are embedded in St Thomas’ Hospital to ensure our research projects are fully aligned with current clinical practice. Long-term collaborations with global MedTech companies and new partnerships with innovative start-ups ensure multiple pathways to translation.

Research at the school is organised into ambitious, large-scale and long-term research projects supported by set of six departments. This diverse infrastructure allows us to combine expertise and apply the latest healthcare concepts to deliver ground breaking results.   

About the role

The AI Centre, led by King’s College London and Guy's and Thomas’ NHS Foundation Trust, is a consortium of multiple NHS Trusts, Universities and UK and multi-national industry partners that has developed platforms allowing the development and deployment of sophisticated artificial intelligence algorithms from NHS data, to provide tools for clinicians to speed up and improve diagnosis and care across several patient pathways.

As part of these platforms, the AI centre created the Federated Learning Interoperability Platform (FLIP), which helps link data from multiple NHS Trusts and enable AI at scale. This platform is now being extended beyond tertiary care to the London Sub-national Secure Data Environment (SNSDE), enabling extended use of patient data across the care continuum.

FLIP requires significant data automation behind the scenes to automatically ingest, curate data, and make it available for algorithmic development. This role will focus exactly on building the algorithmic stack supporting this automation, i.e. building a large scale data harmonisation, labelling and curation platform. More specifically, the role aims to build a system that takes medical imaging data from the PACS, and finds what they are, and harmonises their metadata according to their modality, body part, sequence, pathology, etc, by mapping them into to the RadLex radiology playbook standard. As part of this algorithmic development journey, the applicant is also expected to make fundamental algorithmic contributions in building foundational models, spend time extracting and transforming large amounts of clinical data from our partner hospitals to transform our ability to train large, accurate and robust models.

This is a full time post, and you will be offered a fixed term contract until 31 March 2026.

About You

To be successful in this role, we are looking for candidates to have the following skills and experience:

Essential criteria

• PhD awarded in Mathematics, Engineering or Computer Science or near completion.
• Higher language computer programming (python)
• General machine learning experience (vision, structured data, NLP)
• Machine learning libraries (SciKit Learn, pytorch,…)
• Scientific / Medical Writing
• Ability to work calmly under pressure and act on initiative.
• Interest in medical imaging

Desirable criteria

• Knowledge of software development cycles
• Data analysis packages (Pandas, SciPy/Statsmodels, etc.)
• Visualisation tools (Paraview, OHIF, etc)
• HPC computing services (SGE-like batch-queuing system, Kubernetes, Docker, Containers)
• Independent researcher

Closing date: 08 July 2024

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