Post-doctoral Training Fellow - Image analysis
We seek a highly motivated postdoc to join the Computational Pathology and Integrative Genomics team led by Dr. Yinyin Yuan. This is part of an exciting initiative between ICR and the Arizona Cancer and Evolution Center led by Prof. Carlo Maley (www.maleylab.org) to study cancer evolution and ecology. 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 (www.yuanlab.org). We have pioneered the use of spatial statistics to quantify spatial intra-tumour heterogeneity, a fundamental but under-explored biological feature of tumours. The successful candidate will lead the digital pathology component of a large collaborative program that integrates imaging with omics data.
The successful post holder will provide machine learning expertise for analysing histological images and developing new programs. 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, bioinformatics, and single-cell sequencing, and travel to conferences and collaborators, excel in coordinating between programming and explore new research areas.
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.