PhD studentship: Identifying morphological progression risk factors in oesophageal cancer by combining genome sequencing with histopathology using deep neural networks

This is a unique opportunity for PhD study in the world-leading Cancer Research UK Cambridge Institute (CRUK CI), to start a research career in an environment committed to training outstanding cancer research scientists of the future. The Institute has excellent state-of-the-art facilities and research ranges from basic biology and computational biology through to translational cancer research and clinical application.

Postgraduate students play a pivotal role in the continuing success of our research programmes. If you are interested in contributing to our success, please find further information at: https://www.cruk.cam.ac.uk/jobs-and-studentships/postgraduate-study

Professor Florian Markowetz wishes to recruit a student to work on the project entitled: Identifying morphological progression risk factors in oesophageal cancer by combining shallow whole genome sequencing and histopathology using deep neural networks.

For further information about the research group, including their most recent publications, please visit their website: www.cruk.cam.ac.uk/research-groups/markowetz-group/

Project details

Early detection of risk factors for oesophageal cancer can dramatically increase the survival rate for patients. Doctors often rely on numerous data sources to conclude a diagnosis, including medical history and pathology results. There is evidence that genomic data alone can predict the progression of cancer years before visible symptoms present themselves, however current computational risk prediction methods often only utilise one modality, e.g. histopathology images, or patient data. This project will aim to integrate these multi-modal features for risk prediction of oesophageal cancer by understanding the relationship between genomic data from biopsies, and anonymised patient medical history.

Preferred skills/knowledge

Coding experience and knowledge is essential, preferably in Python. Experience with associated image processing libraries, or PyTorch and other deep learning libraries is preferred.

References/Further reading (optional)

Killcoyne, S., Gregson, E., Wedge, D.C. et al. 'Genomic copy number predicts esophageal cancer years before transformation.' Nat Med 26, 1726-1732 (2020). doi:10.1038/s41591-020-1033-y Mandair, Divneet, Jorge S. Reis-Filho, and Alan Ashworth. 'Biological Insights and Novel Biomarker Discovery through Deep Learning Approaches in Breast Cancer Histopathology'. Npj Breast Cancer 9, 21 (2023), https://doi.org/10.1038/s41523-023-00518-1 .

Funding

This four-year studentship is funded by Cancer Research UK and includes full funding for University and College fees and, in addition, a stipend currently of £21,000 per annum for 4 years.

Eligibility

No nationality restrictions apply to Cancer Research UK studentships

Applications are invited from recent graduates or final year undergraduates who hold or expect to gain a first/upper second-class degree (or equivalent) in a relevant subject from any recognised university worldwide. Applicants with relevant research experience gained through Masters study, or while working in a laboratory, are strongly encouraged to apply.

How to apply

Please apply using the University Applicant Portal. For further information about the course and to access the applicant portal, go to:
https://www.postgraduate.study.cam.ac.uk/courses/directory/cvcrpdmsc Please select to commence study in Michaelmas Term 2024 (October 2024).

To complete your online application, you will need to provide the following:

Reference Request

The names and contact details of two academic referees who have agreed to act on your behalf.

Course Specific Question

Your statement of interest (limit of 2,500 characters) should explain why you wish to be considered for the studentship and which qualities and experience you will bring to the role.

Supporting Document

Please upload your CV (PDF file).

Deadline

The closing date for applications is 31 March 2024 with interviews expected to take place mid to late May.

Please quote reference SW40760 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.