We are seeking a full-time Senior Machine Learning in Medical Imaging Research Software and Systems Engineer to join the Oxford Biomedical Image Analysis Laboratory in a pivotal role working on a number of exciting on-going translational research machine learning in ultrasound imaging projects, involving clinical collaborators in Oxford, as well as overseas partners in India and sub-Saharan Africa. This is a rare opportunity to join an internationally leading inter-disciplinary team developing novel machine-learning based ultrasound image analysis methods and investigating their early deployment and demonstration of proof-of-utility in real-world settings. The post is currently available until 31 October 2022 but may be extended subject to securing further funding. You will be responsible for taking first-proof-of-principle deep learning algorithms and considering their generalization to embedded solutions in clinical demonstrator systems. You will also design, implement and test image processing pipelines for integration into research devices.
You should hold a degree or higher-level qualification in engineering, physics, computer science or related subject, and have experience in building integrated software-based imaging methods and image-based software apps and testing them in real world settings. You should also have an interest in collaborating with end-users/clinical partners to design and develop solutions which may have impact on healthcare practice. Experience of image analysis and deep learning is essential, as well as good written and oral communication skills.
The vacancy is available now, and we are looking to fill this post as soon as possible. Please note that the post cannot be offered as a remote-working position, as the work requires you to access clinical data and use equipment onsite.
Informal enquiries can be directed to Professor Alison Noble (email@example.com).
Only online applications received before midday on 9 August 2021 can be considered. You will be required to upload a covering letter/supporting statement, including a brief statement of research interests, CV and the details of two referees as part of your online application.
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