About the Project
Being able to extract useful information from genetic data is becoming key for biotech and pharmaceutical industries. Although data availability is rapidly increasing, reaching the full potential of this substantial investment relies on our ability to unlock clinically actionable knowledge from it. The key to this is ensuring data access and analysis capabilities are democratised over a range of researchers and institutions with different capabilities, whilst ensuring individual level data is kept safe. However, obstacles to this remain, owing to the data's unprecedented size, complexity, and restricted access.
The hosting research group is in the process of creating a spin-out from the University of Edinburgh. The primary goal for the spin out is to exploit a technology developed within the university that addresses the current challenges on how large genetic data is accessed and efficiently analysed. This project will focus on developing new computational and mathematical methods to efficiently test non-linear links between genetic variants and human diseases. These will be integrated in the computation engine of the developed platform. Non-linear relations can be key in understanding the existent gap between discovered genetic variants and their effect in human traits. The project outputs could lead to both, scientific publications, and the extension of the spin out tool capabilities. At the end of the project, the applicant will have the opportunity to keep the involvement with the spin out.
For further information on how to apply for this PhD opportunity, please visit: https://www.ed.ac.uk/igmm/igmm-graduate-research-and-training/edinburgh-college-doctoral-scholarship-projects
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