|Funding for:||UK Students, EU Students, International Students|
|Funding amount:||From £18,000 The funding covers an annual tax-free stipend tuition fees, and a generous £1200 per year Research Training Support Grant|
|Placed On:||27th May 2022|
|Closes:||24th August 2022|
PhD Studentship - Computational modelling of mutational spread in the healthy ageing tissue
A 4-year EPSRC Doctoral Training Programme PhD studentship is available at the UCL Department of Medical Physics and Biomedical Engineering Hall Lab. The funding covers an annual tax-free stipend (at least £18000 p.a.) tuition fees, and a generous £1200 per year Research Training Support Grant.
As the body ages, cells endure a diverse range of challenges. Unmutated cells compete neutrally in epithelial tissues, causing stochastic clone loss and expansion over time. Over time cells accrue mutations, altering the cell phenotype. These mutations may arise from exposure to external mutagens, or through intrinsic processes of the cell. Mutant cells may gain new properties, or their fitness may change, driving clonal expansion or loss from the tissue. This leads to an evolutionary process within normal tissues of the body, with a heterogeneous population of mutant cells undergoing constant competitive selection. These mutated cells are not themselves cancerous, but may eventually lead to the development of tumours.
Understanding how the tissue remains robust, and how mutations transforms the tissue in carcinogenesis will enable us to identify the fundamental forces that enable and prevent cancer, and support the future development of approaches to better stratify and treat patients. In this PhD project you will use different computational techniques, ranging from developing network models of gene interactions to simulations of cells in the tissue to understand rich experimental datasets shared by collaborators.
The candidate should have a strong background in either physical sciences or life sciences, with evidence of cross disciplinary interests. Programming or bioinformatics experience is desirable, and candidates should have excellent written and presentation skills.
Please complete the following steps to apply:
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