Life Sciences Initiative (LSI) funded PhD Studentship

Application deadline: 15 October 2017

Interviews to be held: Friday 27 October 2017, 14:00 -17:00

Start Date:   Spring 2018

We are pleased to offer the following studentship, funded by QMUL's Life Sciences Initiative. This studentship is open to Home/EU applicants and will be a collaborative project between Barts Cancer Institute and the School of Biological and Chemical Sciences.

Our studentships aim to develop a cohort of scientists equipped both intellectually and technically to conduct the highest quality research on cancer.

Our research degrees are supplemented by a comprehensive support programme, providing training in a wide range of biomedical laboratory methods and other vital transferable skills.

Tackling drug resistance in cancer using evolutionary ecology theory

Primary Supervisor:  Professor Trevor Graham (Barts Cancer Institute)

Secondary Supervisor:  Professor Richard Nichols (School of Biological & Chemical Siences)

Centre for Tumour Biology

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Project summary

Cancer treatment fails because of the evolution of drug-resistant populations within the tumour. To improve patient outcomes, we urgently need a new approach to treatment that is explicitly designed to anticipate and manage the evolution of resistance. There is a repertoire of theory in evolutionary ecology to explain the rate and nature of adaptation to sudden changes in the environment, particularly pesticide use and rapid climate change. This body of evolutionary theory is strongly analogous to that needed to explain the emergence of resistance during cancer therapy – both involve a sudden new selective regime (temperature, presence of a pesticide, or drug). In this PhD project, we propose to translate this theory to understand the development of resistant clones in cancer, and then to construct new treatment regimes that are designed to forestall or even prevent their emergence. The project will involve theory development, bioinformatic analysis of human cancer data and in vitro evolution experiments to critically evaluate the theoretical predictions.

The project is a cross-school collaboration between the School of Medicine (Graham Lab, Barts Cancer Institute) and School of Biological and Chemical Sciences (Nichols Lab), and builds upon Cancer Research UK and Wellcome Trust funding to the Graham Lab.

Full details of the proposed project can be found here.

Who should apply

We are looking for a graduate with an interest in evolutionary theory of cancer drug resistance with, or expecting, at least an upper second class honours degree in a biological or quantitative subject.

Applicants for whom English is not a first language will also require a minimum IELTS score of 6.5 or equivalent, unless their undergraduate degree was studied in and awarded by an English speaking country. For more information on acceptable English language qualifications please click here.

Funding

The studentship includes the following funding for 3 years:

  • A tax free annual stipend in line with the RCUK recommended rate: £16,553 p/a
  • Project consumables
  • Tuition Fees (up to the Home/EU rate only)

How to apply

Application Deadline: Sunday 15 October 2017

To be eligible for this PhD studentship you need:

  • A minimum 2:1 degree in a biological or quantitative subject
  • If English is not your first language, an IELTS score of 6.5 or better.

To apply you will need to submit the below documents to bci-cancercourses@qmul.ac.uk:

  • Completed PhD application form
  • Your CV
  • Statement of purpose
  • 2 references*. At least one of these must be an academic referee.
  • Copy of your transcript(s), including a breakdown of marks
  • Copy of your passport
  • If applicable, proof of English proficiency

*You should contact your referees to request a reference. At least one of your references must be an academic one. References can either be returned to you or submitted directly to bci-cancercourses@qmul.ac.uk using our reference form.

Please ensure you provide all supporting documents, as we are unable to consider incomplete applications.

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