Grade 7: £32,817 - £40,322 p.a.
Nov 08, 2019 12:00PM
We are seeking a full-time Postdoctoral Research Assistant to join the NIH funded CIFASD4 project entitled ‘Image Analysis of Neuro-facial Effects of Prenatal Alcohol Exposure (PAE)’ to work on the development of biomedical image analysis methods to support understanding of foetal alcohol syndrome (FAS) in the womb and early life. You will be a member of the Biomedical Image Analysis Group at the Department of Engineering Science and work in close collaboration with researchers based in the Oxford Big Data Institute and Nuffield Department of Women’s and Reproductive Health. The post is available now and fixed-term until project end which is 31 May 2021. You will assist in the development and implementation of novel machine learning based image analysis algorithms to automatically and accurately analyse the content of foetal neurosonography images to discover differences between the brains/faces of foetuses with prenatal alcohol exposure and healthy foetuses. You will also collaborate with CIFASD consortium members and other partners to interpret their data and explain findings.
An aim of this particular project is to undertake neuro-facial analyses of foetal facial and brain structures derived from 2D and 3D volumetric ultrasound images that have been acquired by a collaborating network. Medical image analysis methods are also being developed to look at neonatal brains affected by FASDs. Machine learning based analysis approaches are being developed to gain greater insight into how development in the healthy and FAS foetal brain differ and how neurological structure development and facial features in the developing FAS foetus are correlated. 2D and 3D ultrasound-based software tools are being produced to support researchers in performing studies on cohorts which include antenatal ultrasound assessment.
You should possess a doctorate, or near completion of doctorate, in deep learning applied to biomedical image analysis or computer vision, as well as a background or expertise in designing deep learning algorithms and their evaluation. Expertise with deep learning tool kits and understanding of key mathematical tools such as linear algebra and real analysis to the end of understanding advanced machine learning and computer vision/biomedical image analysis algorithms is also essential for this role. Experience in collaborating with clinicians is desirable as is a keen interest in designing and developing solutions which may have impact on healthcare practice in the future.
Informal enquiries can be directed to Professor Alison Noble using the email address provided below. Further information can be found at: www.eng.ox.ac.uk/about-us/jobs
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.
Only applications received before 12.00 noon on Wednesday 25 September 2019 can be considered.
The Department holds an Athena Swan Bronze award, highlighting its commitment to promoting women in Science, Engineering and Technology.
Professor Alison Noble
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