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: https://www.mrc-cu.cam.ac.uk/research/Shamith-Samarajiwa-folder
Samarajiwa lab: https://www.samarajiwa-lab.org
How to apply
Applications will need to be made through the University Application Portal and will entail an application fee of £65. Please visit: http://www.graduate.study.cam.ac.uk/courses/directory/cvcupdmsc/apply
for further information about the programme and to access the Applicant Portal. Please note that the course code for PhD applications to the MRC Cancer Unit is MDCU22. Whilst making your online application please make it clear which project area(s) and principal investigator(s) you are interested in working with. Your online application needs to include:
• A CV, including full details of all University course grades to date.
• Contact details for two academic or professional referees.
• A personal statement outlining your interest in a specific project area, what you hope to achieve from a PhD, and your research experience to date.
The above information must be provided under relevant sections on the application portal.
Completing the Research section
If you are applying for one project only: in the ‘Proposed title of Research’ textbox, insert the project title. If you wish to apply for more than one project, insert ‘Cancer Research’.
If you are applying to more than one supervisor: in the ‘Proposed Supervisor’ textbox, insert the initials of the Supervisors you wish to consider your application and in the ‘Research Summary’ textbox, insert the project titles being offered by those Supervisors.
Please describe your research experience in the appropriate textbox.
Interviews are expected to take place in December.
Please contact [email protected]
with any other enquiries concerning studentships or eligibility criteria.