PhD Studentship

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Applications are invited for a PhD studentship in Opportunistic Analysis of Radiological Screening. The post is based in the University of Cambridge Department of Radiology on the Cambridge Biomedical Campus. The proposed start date is October 2024.

Project Details:

Opportunistic analysis of radiological screening involves the analysis of imaging from abdominal and thoracic examinations that are typically unrelated to the main clinical reason for the scan. Most of this incidental imaging data has been underutilised, yet it holds promise in enhancing patient health by aiding in prevention, risk assessment, and early detection of diseases. Evidence is mounting on the potential benefits of the detailed analysis of such data, particularly those related to body composition, in helping clinicians estimate the biological age of patients and forecasting potential cardiometabolic issues. These capabilities could match or even surpass existing clinical benchmarks. This project will initially utilise existing open-source software for deep learning-based segmentation for the rapid retrospective and prospective analysis of routinely acquired abdominal CT images. The segmentations will then be used to extract several quantitative biomarkers from the images, such as visceral and subcutaneous fat volumes, muscle volume, liver volume and vascular calcification. The objective of the project will be to develop a workflow where these biomarkers can be extracted entirely automatically and provided back as a structured report for clinicians to review. The various organ segmentations will then be used in a radiomics-based analysis where a multitude of latent image 'texture' features will also be extracted and compared between patients to investigate whether such image features can also be leveraged to reveal opportunistic detection of disease, such as liver fibrosis or steatosis. Further development of the project will involve applying similar approaches to opportunistic MRI data acquisitions. See the following open access article for further information on opportunistic CT screening

We recommend you visit the Student Funding Search for further details about this project, including the annual stipend:

Studentship in Opportunistic Analysis of Radiological Screening - Postgraduate Funding Search (

What we are looking for:

Candidates should have or expect to obtain a first or good 2:1 degree in physics, engineering, or computer science. A demonstrable interest in machine/deep learning and working with health-care related data is essential. Applicants with relevant research experience, gained through Master's study or laboratory work, are strongly encouraged to apply. Motivation, creativity and intellectual independence are desirable, as are excellent communication skills and the ability to work collaboratively.

Studentship Details:

The funding for this post is available for 3 years. Funding covers the student's stipend and tuition fees at the Home rate. International applicants will be considered only if they can fund the overseas fees differential or if they are awarded a suitable scholarship.

How To Apply:

Applications are made via the University Applicant portal (Applicant Portal and Self-Service account ' Postgraduate Study ( Please apply for the PhD in Radiology with 'Opportunistic Analysis of Radiological Screening' as the project title. You do not need to contact potential supervisors in advance or prepare a research proposal.

The application deadline is Sunday 10th March 2024.

Informal enquiries can be addressed to: Professor Martin Graves ¿

Questions about the application process should go to:

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.

Please quote reference RQ40453 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.

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