Research Associate (Fixed Term)

We wish to appoint a physicist or data scientist with relevant experience and skills in MRI and/or machine learning to work with the Department of Radiology. The department has access to state-of-the-art imaging facilities and has an established record in developing novel technologies.

This post is split 50% between two roles between the Department of Radiology, School of Clinical Medicine (SCM), and the Cambridge Institute for Medical Research (CIMR).

Fifty percent of the role is for developing machine learning algorithms for differentiating two lung disease states based on CT images. The ideal candidate will have had previous experience with machine learning for medical imaging. The machine learning algorithm will be incorporated into federated learning platform and be used to analyse data across multiple sites on the same dataset. This portion of the post is joint with CIMR, under the supervision of Professor Stefan Marciniak and Dr Joshua Kaggie. The other fifty percent of this post has roles to work on the development and validation of MR-based measures of quality, which will include working with retrospective datasets, phantom testing on different MR scanners, and assessing reproducibility of measurements. Additional work involves working on low-risk prostate cancer follow-up studies (Active Surveillance) datasets for external validation of a machine learning tool, with integration of clinical data.

The successful candidate will be expected to have excellent computational skills and to integrate with an enthusiastic multidisciplinary group of people with the aim of translating this technique into the clinical arena, research experience in pulse sequence development is also desirable. Therefore, applicants should hold a Ph.D. and ideally have post-doctoral experience involving the development and application of magnetic resonance methods

Please ensure you read the Further Particulars in full prior to submitting an application.

Fixed-term: The funds for this post are available until 1 December 2025 in the first instance.

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Please ensure that you upload a covering letter and a CV in the Upload section of the online application. The covering letter should outline how you match the criteria for the post and why you are applying for this role. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application.

Please include details of your referees, including email address and phone number, one of which must be your most recent line manager.

The closing date for applications is: 29th May 2024

The interview date for the role is: To be confirmed

Please quote reference RQ41678 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

Further information
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