MRC Cancer Unit PhD Studentship

Studentship Tier 2 Jobs Recruiting in PhD/ Research
  • University Of Cambridge, Cambridge, UK View on Map
  • Post Date: October 5, 2019
  • Apply Before : November 15, 2019
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Job Detail

  • Career Level Student
  • Experience Fresh
  • Gender Any
  • Industry Research/ Development
  • Qualifications PhD Requirements
  • Email [email protected]

Job Description

The Medical Research Council Cancer Unit at the University of Cambridge is a leading centre for cancer research in the UK. Our aim is to undertake research that advances our understanding of the earliest steps in the emergence of cancer, and to use this knowledge for early diagnosis, risk stratification and clinical intervention, through the development of innovative enabling technologies. The Unit is based within the Hutchison/MRC Research Centre on the Cambridge Biomedical Campus, and possesses excellent research facilities, strong collaborations with clinicians and colleagues in other disciplines, and a vibrant and supportive working environment.

We have the following PhD projects on offer for entry in October 2020, with funding available through studentships from the Medical Research Council. Eligibility and Funding criteria apply and are stated below.

Uncovering the (epi)Genomic regulation of cancer forming processes using integrative computational genomics and deep learning approaches – Dr Shamith Samarajiwa

Mutations in oncogenes and tumour suppressor genes, copy number changes and other genetic aberrations, together with anomalous epigenomic modifications all conspire to alter gene expression programmes, perturb normal cellular processes and promote tumour formation. Perturbation of signaling pathways and networks, transcription factor binding patterns, reconfiguration of the three dimensional chromatin architecture, and changes in regulatory element activity and interactions enable tumour formation and cancer progression. Understanding these processes will enable development of therapies as well as development of cancer early detection applications. This project will use integrative computational approaches and large biomedical data-sets to develop and apply computational biology and machine learning (including novel deep learning solutions) methods to understand the complex systems involved in cancer formation and progression.

This project will bring together big data analytical, modelling, data-mining and visualisation approaches.

Novel integrative methods will be developed and applied to multi-omic cancer datasets. Unique data integration approaches will be applied including modelling biological systems as knowledge graphs.

Cutting edge computational biology and genomics approaches will be combined with deep learning methods (Convolutional neural networks (CNNs), generative methods such as variational autoencoders (VAEs) and generative adversarial networks (GANs) together with other machine learning methods.


A masters degree in a quantitative field (data science, machine learning, deep learning, computational biology, mathematics, statistics or engineering) is required. Candidates with Biomedical or Medical degrees with exceptional (R and/or Python) programming and analytical skills with a good understanding of cancer biology, immunology or metabolism are also welcome to apply.

More information about the research undertaken in the Samarajiwa group can be found here: Samarajiwa lab:

Eligibility and Funding

We welcome applications from those holding or expecting to obtain at least an upper second class degree (or equivalent) in a relevant scientific subject. These studentships are funded by the Medical Research Council and are open to UK and EU applicants only. Other international students are not eligible to apply. UK applicants will be eligible to receive full funding of University and College fees and a stipend of £18,000 p.a. EU applicants will be funded on a fees-only basis, unless they meet the MRC’s eligibility criteria for residency (visit the MRC website for further details: Successful applicants will be registered with the University of Cambridge.

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