Research Associate in Deep Learning and Fetal Neurosonography

Full time (37.5 hours/ week)

Fixed-term contract until 31 May 2027 (with possibility of extension)

A postdoctoral research associate position is available for a technically strong researcher to join the Oxford Machine Learning in NeuroImaging (OMNI) lab at Oxford’s Department of Computer Science, focusing on high-performance deep learning for neural implicit reconstruction of ultrasound data. The goal is to advance the scalability and efficiency of neural radiance fields (NeRFs) and related architectures to enable near real-time 3D reconstruction from 2D ultrasound video.

The post-holder will contribute to cutting-edge research at the intersection of deep learning, computer vision, and biomedical imaging. This includes exploring efficient network designs, contributing to the development of novel learning-based representations for geometric reconstruction, and integrating insights from neural rendering into medical imaging workflows. A major focus will be on accelerating inference and training using GPU-optimised components, including custom CUDA kernels.

This role offers a unique opportunity to push the boundaries of neural scene representations in a medical imaging context. The successful candidate will work alongside a multidisciplinary team of deep learning researchers, computer vision experts, and clinicians, designing scalable and responsive tools that directly support prenatal brain imaging at the bedside. The candidate will also benefit from co-supervision by Dr Joao Henriques from the Visual Geometry Group (VGG).

The successful applicant will report to the project PI, Professor Ana Namburete. The position is available from September.

Flexible working

This is a full-time role (37.5 hours per week) that requires on-site working. Some flexibility may be possible, depending on work needs, such as attending in-person meetings or travelling for conferences.

What We Offer

As an employer, we genuinely care about our employees’ wellbeing and this is reflected in the range of benefits that we offer including:

• An excellent contributory pension scheme
• 38 days annual leave (pro-rata for part-time roles)
• A comprehensive range of childcare services
• Family leave schemes
• Cycle loan scheme
• Discounted bus travel and Season Ticket travel loans
• Membership to a variety of social and sports clubs

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 noon on 29^th Septemebr 2025. Interviews are expected to be held in October.

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