AffiliationsCentre for Evolution and Cancer & Division for Molecular Pathology,
The Institute of Cancer Research, London
Centre for Molecular Pathology,
The Royal Marsden Hospital
A variety of opportunities are available. Qualification in computer science, statistics, ecology or other quantitative disciplines is usually required. Good programming skills, preferably in R, Matlab, Python or C, and experience in image analysis, machine learning or statistics are essential. All positions except for the student scholarship are immediately available, but start dates are negotiable. Enquiry can be sent to yinyin.yuan (a) icr.ac.uk, but applications need to be sent through ICR website for:
Image analysis postdoc position to investigate treatment resistance
in 4,500 breast cancers in collaboration with Prof Mitch Dowsett's
team at the ICR (deadline 30/01/2017 with a potential start date of 01/03/2017).
Officer to lead the development
of bioinformatics pipelines for
integrative analysis of digital pathology and genomic data (deadline 18/02/2017 with a potential
start date of 01/04/2017).
Summer intern scholarship
A 8-weeks project that will be customised based on the
skill set and interests of the successful candidate (deadline 27/02/2017).
analysis postdoc position to work on TRACERx, the
largest study of lung cancer evolution to date in a collaboration with Prof
Charles Swanton at the Francis Crick Institute (deadline 01/03/2017 with a potential
start date of 01/05/2017).
Khan AM, Yuan Y*
Nature Scientific Reports (2016)
We systematically investigate biopsy variability for the lymphocytic infiltrate in 998 breast tumours using a novel virtual biopsy method. Interestingly, biopsy variability of lymphocytic infiltrate differs considerably among breast cancer subtypes, with the HER2+ subtype having the highest variability.
Natrajan R, Sailem H, Mardakheh FM, Arias MG, Dowsett M, Bakal C, Yuan Y*
PLOS Medicine (2016) [Sweave; R package beta version]
We propose a clinically relevant role of tumour microenvironmental diversity for advanced breast tumors and highlight that ecological statistics can be translated into medical advances for identifying new biomarkers and for understanding the synergistic interplay between the microenvironment and cancer genomics.