We are currently looking for a Research Associate in Multi-view 3D Ultrasound Reconstruction in the Oxford Machine Learning in Neuroimaging (OMNI) lab at Oxford's Department of Computer Science, funded by the European Research Council through the WOMB2COT Starting Grant. The post-holder will develop 3D reconstruction and multi-view data fusion methods for neonatal cranial ultrasound (cUS), and validate them on data collected in the neonatal intensive care unit (NICU).
Reporting directly to Professor Ana Namburete, the successful candidate will work within a multidisciplinary team of deep learning researchers, research software engineers, and neonatologists, and will collaborate with Dr Frances O'Brien (Consultant Neonatologist, Oxford University Hospitals NHS Foundation Trust).
The post-holder will contribute to research at the intersection of 3D computer vision, neural rendering, and medical image analysis. This includes physics-informed rendering models that account for view-dependent acoustic appearance, and working with multi-fontanelle data acquired by clinical colleagues.
This post is based at the Department of Computer Science.
About Us
The University of Oxford is a stimulating work environment, which enjoys an international reputation as a world-class centre of excellence. Our research plays a key role in tackling many global challenges, from reducing our carbon emissions to developing vaccines during a pandemic.
The Department of Computer Science at Oxford is renowned for pioneering research and teaching across diverse fields, consistently ranking among the best in the world. Our commitment to innovation drives us to tackle complex technological and societal challenges.
What We Offer
As an employer, we genuinely care about our employees' wellbeing, reflected in the range of benefits that we offer including:
The successful candidate will be based onsite in the Department of Computer Science buildings in central Oxford. However, remote working from within the UK and flexible working arrangements can be considered.
Diversity
Committed to equality and valuing diversity.
Application Process
You will be required to upload a supporting statement and an up-to-date CV as part of your online application. Your supporting statement must clearly demonstrate how you meet each of the essential selection criteria listed in the job description. Applications that do not include a supporting statement or CV, or fail to address the criteria in sufficient detail, will not be considered.
While we recognise the value of AI tools in assisting with application preparation, submissions that are clearly AI-generated without personalisation or insight will be rejected. It's crucial that your application reflects your own experiences and understanding of the role.
The closing date for applications is midday on 23 November 2026. Interviews are expected to be held in early December.
Tagged as: Life Sciences
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