Research Associate / Senior Research Associate in Computer Vision and Machine Learning (Medical Imaging)

University of Bristol

London

1 month ago

Applications closed

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The role

To work as a Research Associate / Senior Research Associate on a project to develop and validate technology to enhance orthopaedic surgical planning workflows, through advanced analysis of clinical imaging data and development of clinician-facing software.
We are seeking motivated post-doctoral candidate to join this exciting collaborative project and a team of orthopaedic surgeons, engineers, and computer scientists based between University of Bristol and University of Oxford.

What will you be doing?

The postholder will have responsibility for leading the technical delivery of the work including the development, training, and testing of computer vision and machine learning tools for AI-automated anatomical landmark detection and segmentation of clinical images. Building upon previous work of the group, the postholder will also be responsible for the integration and optimisation of this code into a clinician-facing annotation and surgical planning software package, which include work in user-interface design and planning for future deployment in the health service. This is a research-intensive role with scope for significant technical leadership, interdisciplinary collaboration, and contribution to research outputs, software, high-quality publications, and clinical translation.

You should apply if

You have:

A PhD (or equivalent research experience) in computer science, biomedical engineering, machine learning, robotics, or a related discipline.
For Senior Research Associate: Substantial post-doctoral / industry experience. Strong Python programming and software engineering skills
Demonstrated expertise in one or more of: Computer vision and Machine learning in development of AI for anatomical landmark detection and/or finite element modelling
2D/3D image segmentation using deep learning
3D–2D or multimodal image registration
A track record of high-quality research outputs

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