Postdoctoral Training Fellow - Image Processing

20/08/2020, 23:55

Sutton
Molecular Pathology
Computational Pathology & Integrated Genomics
Full time
Fixed Term
3 years
35
£32,200 - £42,550
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 five 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 highly motivated Postdoctoral Training Fellow to join the Yuan lab at The Institute of Cancer Research, London (ICR). The main focus of the Yuan lab is to develop novel computational approaches for studying cancer by fusing machine learning, digital pathology, and bioinformatics (www.yuanlab.org). We apply advanced deep learning and computer vision to study why cancer is so difficult to treat, by examining cancer evolution dynamics in the context of the tumour microenvironment. Our recent progress on AI, histology and cancer evolution was published in the journal of Nature Medicine (doi: 10.1038/s41591-020-0900-x).

This is a rewarding opportunity to participate in the Cancer Research UK funded Early Detection program, to define risk in smouldering myeloma for early detection of multiple myeloma. The aim is to develop better markers and therapies to predict, and ultimately prevent, progression from this pre-cancer status to incurable cancer.  

We are looking for a highly motivated individual to join this project. You will lead the development of new deep learning systems for analysing digital pathological images of smouldering myeloma. You will enjoy the highly collaborative nature of this project, working with the internationally renowned team consisting of oncologists, pathologists, immunologists, bioinformaticians at UCL, Broad Institute, and Dana-Farber Cancer Institute to deliver leading-edge toolkits to identify the genomic, transcriptomic and immunological landscape of smouldering myeloma. You will be located in the vibrant centre of cancer research discovery and therapeutics at ICR London in the endeavour to cure cancer. You will learn about the latest biotechnologies, travel to conferences, and excel in coordinating between programming and biomedical research.

Applicants must hold a PhD in Computer Science, Systems Biology, Ecology, Statistics or Engineering. Good programming skills, preferably in R, Matlab, Python or C, and experience in computer vision, machine learning or statistics are essential. Experience in biomedical image processing and deep learning is desirable but not essential.

Appointment will be on a Fixed Term Contract for 3 years, with a starting salary in the range of £32,200 to £42,550 p.a. inclusive, depending on postdoctoral experience. Expected start date is 1 September 2020 with flexibility. The successful candidate will be based in Sutton, Greater London.

To apply please include the following to your application:

  • A full CV with a publication list
  • Research plan (one page outlining your current research interests and research plans for the next 3 years) – this can be uploaded in to the Supporting Statement section

To apply, please complete an online application including the supporting statement, contact details of two referees, and upload your CV.

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

Funded by

Cancer Research UK
Additional Documentation for Candidates