Launch of MRC Precision Medicine Doctoral Training PhDs
[21 Mar 2016] The University of Edinburgh, in collaboration with the University of Glasgow and the Karolinska Institute, received a prestigious MRC award for a Doctoral Training Programme in Precision Medicine, a new and exciting programme integration between informaticians, clinicians and life scientists.
Over 40 potential PhD projects have been launched, covering an incredible range of topics including: using robot interaction to aid dementia sufferers; developing molecular tools for early diagnosis of arthritis, and; using new techniques and Big Data to investigate a range of issues including diabetes and asthma.
About the programme
This new Doctoral Training Programme will focus on training PhD students in key MRC skills priorities in quantitative skills (mathematics, statistics, computation, developing digital excellence) as applied to variety of data sources (from ‘omics’ to health records), and interdisciplinary skills including imaging and stratified medicine.
The vision of this programme is to support research training at the interfaces between biological, clinical, societal and computational systems. Mandatory taught elements will include statistics, research ethics, innovation and entrepreneurship, health economics, data management and bioinformatics.
Applying for a project
Eligible UK nationals, and eligible EU nationals who can prove three years of residence in the UK, should apply using the University of Glasgow's application process. Please note that you will be registered at the host institution (Edinburgh or Glasgow) of the primary supervisor detailed in your project selection. Deadline for applications is April 4, 2016.
Please note the deadline for applications has now passed.
A complete list of projects, and more information about the application process, is available on FindAPhD.com.
FindAPhD.com - Complete List of Projects
Projects with supervisors based in the Usher Institute
Assessment of clinical discriminatory performance of CRC pathological staging and related uncertainty using omic data and Bayesian methodology
Human-Robot Interaction for Dementia Prevention and Research
Identification of therapeutically-relevant patient subgroups from clinical and biological data
Identification, functional characterization and in vivo mouse CRISPR transgene modeling of novel human genome wide association variants for metabolic disease/ageing
Identifying new diabetic retinopathy biomarkers based on Optical Coherence Tomography Angiography (OCT-A), advanced image processing, and computational modelling
Stratifying Type 1 diabetes by residual insulin secretion: implications for risk management, therapy and disease prevention
Use of -omics and retinal image data to improve vascular risk prediction in type 2 diabetes
Using Big Data to identify precision medicine targets for asthma
Using genome sequence data to combat antimicrobial resistance
University of Edinburgh: Precision Medicine