Application of machine learning to drive preclinical cancer drug discovery
Supervisors: Dr James Bradford (Department of Oncology) and Professor Neil Lawrence (Department of Computer Science)
A PhD student is sought with a strong computational background and a keen interest in applying their skills to cancer research
Pre-clinical cancer models such as cell lines or xenografts are critical to the development of anti-cancer therapeutics and advancing our understanding of cancer biology. They are consistently used as a platform to investigate therapeutic mechanism of action and identify potential biomarkers prior to clinical trials in which similar exploration is often complicated, unethical and expensive. However, their applicability in certain cancer settings has been frequently questioned due mainly to the difficulty of aligning the appropriate model with a clinically relevant disease segment.
The focus of the studentship will be to integrate emerging “big data” from numerous large- scale Next Generation Sequencing tumour profiling efforts using cutting edge machine learning methods to enable accurate and efficient evaluation of the clinical relevance of a given pre-clinical model. The project is anticipated to have significant impact on the early cancer drug discovery process, particularly in light of the current drive towards personalised healthcare, ensuring that early testing of new therapeutics is carried out using only pre-clinical models accurately aligned to the relevant disease segment.
The project represents an exciting and significant computational challenge that will draw from world-class machine learning expertise in Sheffield. The successful candidate will benefit from state-of-the-art high performance computer facilities, and access to a cross-departmental network of computational biologists through the Sheffield Bioinformatics Hub. Training and development will strongly reflect the multi-disciplinary nature of the project enabling key expertise to be built in computer science, oncology and bioinformatics.
This is 3.5-year studentship covering UK/EU tuition fees, and an annual, tax-free maintenance stipend at the standard Research Council rate (£13,863 in 2014-15).
Applicants should have, or expect to achieve a first or upper second class UK honours degree or equivalent in a relevant area of study (e.g. computer science, bioinformatics etc). The award is open to UK/EU applicants only.
How to apply
In the first instance, informal enquires can be made to Dr James Bradford.
Candidates are required to complete an application for admission as a postgraduate student
Applications should include a CV, two references, and a supporting statement that demonstrates a clear interest in the project and justification for being considered.
The closing date for applications is 19th September 2014.