Research Associate in Machine Learning to Predict the Risk of Breast Cancer

Manchester, United Kingdom
£32,236 to £34,189 per annum (according to relevant experience)
07 Oct 2018
End of advertisement period
19 Oct 2018
Contract Type
Fixed Term
Full Time

Location : Oxford Road, Manchester
Closing Date : 19/10/2018
Salary : £32,236 to £34,189 per annum (according to relevant experience)
Employment Type : Fixed Term
Faculty / Organisational Unit : Biology, Medicine & Health
Division : Informatics, Imaging & Data Sciences
Hours Per week : Full Time
Contract Duration : ASAP for 14 Months from start date

Applications are invited for a Research Associate in Imaging Science, in the Division of Informatics, Imaging and Data Sciences. The post is available immediately and will be tenable on a fixed term basis for 14 months.

You will join the Centre for Imaging Sciences and take responsibility for a defined area of research, under the supervision of Dr Sue Astley. You will develop and evaluate software to quantify mammographic density in ultra-low-dose mammograms. Mammographic density is related both to an individual’s risk of developing breast cancer, and to the likelihood that early signs of cancer in a woman’s breast will be detected by mammographic screening. A safe and effective method for assessing breast density in young women will allow personalisation of screening, so those with dense breasts and a high risk of developing breast cancer can be screened using additional imaging modalities, increasing the chance of early detection of signs of abnormality.

You will build on our existing work using machine learning to identify regions of fatty tissue, model the shape of the compressed breast and calibrate the pixel values in standard mammograms. You will use deep machine learning methods to define the relationship between standard mammograms and their ultra-low-dose counterparts, training convolutional neural networks (CNN) on both real and simulated low dose images, and evaluating all methods systematically. In addition, you will use similar methods assist with the analysis of breast density data from a study of the efficacy of diet and exercise weight loss in women with a family history of breast cancer.

You should have previous experience in computer vision, medical image analysis, machine learning, or AI, with a PhD or equivalent experience in one of these areas. You should also have previous experience in programming in C, C++, Matlab, Python or similar languages. Experience of scientific algorithm/methods design and statistical evaluation, and a developing publication record, would advantageous.

The School is strongly committed to promoting equality and diversity, including the Athena SWAN charter for gender equality in higher education. The School holds a Silver Award which recognises their good practice in relation to gender; including flexible working arrangements, family-friendly policies, and support to allow staff achieve a good work-life balance. We particularly welcome applications from women for this post. Appointment will always be made on merit. For further information, please visit:

Please note that we are unable to respond to enquiries, accept CVs or applications from Recruitment Agencies

Enquiries about vacancy shortlisting and interviews:

Name: Dr Sue Astley


Technical support:


Tel: 0161 850 2004

General enquiries:


Tel: 0161 275 4499

This vacancy will close for applications at midnight on the closing date

Further Particulars and Job Description

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