Post-doctoral Training Fellow - Image analysis

15/07/2018, 23:55

Molecular Pathology
Computational Pathology & Integrated Genomics
Full time
Fixed Term
3 years
£30,410 - £40,008

We seek a highly motivated postdoc to join the Computational Pathology team led by Dr. Yinyin Yuan in a collaborative project with a leading cancer evolution team led by Prof. Carlo Maley at Arizona State University. 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.

The main focus of the Yuan lab is to develop new computer vision and deep learning tools for large-scale analysis of tumour pathological images ( We have also pioneered the use of spatial statistics to capture the rich spatial information in these images. The successful candidate will lead the development of pathological image analysis as part of an international collaborative program for studying cancer heterogeneity and evolution through the integration of automated image analysis with omics data.

The successful post holder will provide machine learning expertise for analysing histological images. He/she will enjoy the highly collaborative environment at ICR and in this research program, and work closely with an international, highly interdisciplinary team. He/she will have the opportunities to learn about the cutting-edge technologies including deep learning and single-cell sequencing, and travel to conferences, excel in coordinating between programming and explore new research areas in medicine through collaborations.

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

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

This Program is closed to applications.