Senior Research Scientist
DeepHealth
Senior Research Scientist
DeepHealth
AI-driven medical imaging and informatics platform
Job Summary
The Senior Research Scientist sits on DeepHealth's ML Capabilities team and advances research in machine learning for medical imaging, with a focus on foundation models in radiology AI. This role designs novel model architectures, develops experimental systems to test research hypotheses, and helps translate research into solutions that support clinical applications. The work draws on DeepHealth's large-scale imaging data and expert feedback loops and includes collaboration with research partners such as Imperial College London.
Essential Duties and Responsibilities
Define and drive the scientific roadmap for foundation models in medical imaging within the ML Capabilities programme.
Track and evaluate developments in machine learning and generative AI relevant to radiology and multimodal imaging.
Design and prototype novel model architectures for multimodal medical imaging data.
Develop experimental systems to validate research hypotheses and measure progress against scientific goals.
Establish and maintain benchmarking frameworks across downstream clinical and technical tasks.
Collaborate with engineering teams to translate research advances into deployable solutions.
Lead scientific publications and conference contributions that communicate DeepHealth research results.
Engage with external research collaborators, including partners at Imperial College London.
Mentor researchers and contribute to research quality standards within the ML Capabilities team.
Document methods, experiments, and findings to support reproducibility, review, and knowledge transfer.
PLEASE NOTE: This is not an exhaustive list of all duties, responsibilities and requirements of the position described above. Other functions may be assigned and management retains the right to add or change duties at any time.
AI-driven medical imaging and informatics platform
DeepHealth provides AI-powered medical imaging and health informatics solutions that help radiologists analyze X-rays, CTs, and MRIs by automating parts of image interpretation, highlighting areas of concern, and delivering quantitative insights. It works by integrating AI into radiology workflows via cloud-based software, enabling automated image analysis, scoring, and reporting within enterprise imaging systems. The company differentiates itself through its integration with RadNet and strategic acquisitions of iCAD and CIMAR UK, expanding breast imaging capabilities, interoperability, and the scale of its deployments. Its goal is to accelerate the adoption of AI in real-world healthcare, improve diagnostic accuracy and consistency, and streamline imaging workflows across hospitals and health systems.
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