Senior ML Scientist: 3D Medical Imaging & Foundation Models in Cambridge

Senior ML Scientist: 3D Medical Imaging & Foundation Models in Cambridge

Cambridge Full-Time 63000 - 77000 Β£ / year (est.) No working from home possible
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At a Glance

  • Tasks: Develop cutting-edge 3D medical imaging models and collaborate with experts in the field.
  • Company: Qureight, an innovative AI-driven imaging platform based in Cambridge and London.
  • Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
  • Other info: Be part of a dynamic team driving real change in healthcare.
  • Why this job: Join a mission to revolutionise clinical trials with impactful AI technology.
  • Qualifications: Experience in machine learning and a passion for medical imaging.

The predicted salary is between 63000 - 77000 Β£ per year.

Qureight, headquartered in Cambridge and London, is scaling its AI-driven imaging platform to accelerate clinical trials. We seek a Senior Machine Learning Scientist to develop state-of-the-art 3D medical imaging models, focusing on foundation-model pre-training, segmentation, classification, and biomarker extraction, collaborating with ML engineers and clinicians to deliver reproducible, clinically meaningful outputs.

Senior ML Scientist: 3D Medical Imaging & Foundation Models in Cambridge employer: Qureight

Qureight is an exceptional employer that prioritises employee well-being and professional growth, offering a supportive and diverse work environment. As a Senior Clinical Site Manager, you will benefit from competitive perks such as an annual bonus and private medical insurance, while also having the opportunity to lead and mentor junior team members in the dynamic field of imaging trials. Join us in making a meaningful impact in clinical research at our innovative location.

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Contact Details:

Qureight Recruitment Team

We think you need these skills to ace Senior ML Scientist: 3D Medical Imaging & Foundation Models in Cambridge

Machine Learning
3D Medical Imaging
Foundation Models
Model Pre-Training
Segmentation
Classification
Biomarker Extraction