Postdoctoral Training Fellow - Image Processing

24/03/2019, 23:55

Sutton
Molecular Pathology
Computational Pathology & Integrated Genomics
Full time
Fixed Term
2 years
35
£31,023 - £36,798
The Institute of Cancer Research, London, is one of the world’s most influential cancer research institutes, with an outstanding record of achievement dating back more than 100 years. We provided the first convincing evidence that DNA damage is the basic cause of cancer, laying the foundation for the now universally accepted idea that cancer is a genetic disease. Today, The Institute of Cancer Research (ICR) leads the world at isolating cancer-related genes and discovering new targeted drugs for personalised cancer treatment.

Under the leadership of our Chief Executive, Professor Paul Workman FRS, the ICR is ranked as the UK’s leading academic research centre. Together with our partner The Royal Marsden, we are rated in the top four cancer centres globally.

The ICR is committed to attracting, developing and retaining the best minds in the world to join us in our mission – to make the discoveries that defeat cancer.

We seek a motivated postdoc to join the Computational Pathology and Integrative Genomics team led by Dr. Yinyin Yuan in a highly interdisciplinary project with Prof. Janet Shipley. ICR were ranked first in the Times Higher Education league table of UK university research quality from the most recent Research Excellence Framework (REF 2014) for our high impact publications. We are world leaders in identifying cancer genes, discovering cancer drugs and developing precision radiotherapy. Together with our hospital partner The Royal Marsden, we are rated in the top four centres for cancer research and treatment worldwide. Our Cancer Research UK Cancer Therapeutics Unit is the largest academic cancer drug discovery and development group worldwide. We discover more new cancer drugs than any other academic centre in the world.

Rhabdomyosarcomas are rare cancers but a major cause of death from cancer in children. Looking at cancer cells down a microscope is traditionally used for diagnoses and contributes to deciding treatment. However, many biological features are not currently objectively assessed that are likely to impact on the clinical course of rhabdomyosarcoma. This project aims to develop the first tailored state-of-the-art digital image processing approach using artificial intelligence to determine factors that predict clinical behaviour and outcome.

The Yuan lab at ICR develops pioneering deep learning approaches for the analysis of histopathological images and explores new ways to integrate image data with genomics (www.yuanlab.org). We specialise in the application of spatial statistics to harness rich spatial information in these images.  

The successful post holder will develop new image processing pipelines for samples from 405 rhabdomyosarcomas patients enrolled in clinical trials with genetic data. He/she will enjoy the highly collaborative environment at ICR and in this research program, and work closely with a highly recognised team in sarcoma molecular biology. He/she will have the opportunities to learn about latest cutting-edge biotechnologies/methods including deep learning and cancer genetics, travel to conferences and partner institutes, excel in coordinating between programming and explore new research areas in medicine through collaborations.

Applicants must hold a PhD in Computer Science, Bioinformatics, Engineering, Ecology, Physics, Mathematics or Statistics. Knowledge/experience in biomedicine or deep learning is desirable but not essential.

Appointment will be on a Fixed Term Contract for 2 years, with a starting salary in the range of £31,023 to £36,798 p.a. inclusive, depending on experience. The successful candidate will be based in Sutton, Surrey.

To apply, please upload your CV and covering letter online via the ICR website.

Please read the job description for further details.

We consider all applications on merit and have a strong commitment to enhancing the diversity of our staff.
Additional Documentation for Candidates