Artificial intelligence, machine learning and precision healthcare: pseudotemporal ordering to identify latent disease trajectories in cancer progression

The Genomics England 100,000 Genomes Project (100KGP) is a transformative initiative of national importance in prototyping and understanding the potential role that genome technologies will play in the NHS (https://www.genomicsengland.co.uk/). A critical aspect of the project will be the development of computational tools that will allow scientific insights to be extracted from the genomic data. These analyses will inform the development of information systems that will permit the use of complex molecular data in routine patient management for a wide range of human diseases.

This project will involve the design of statistical machine learning and artificial intelligence methods for modelling patient disease progression trajectories specifically in cancer. The ultimate goal is to construct predictive models for precision medicine applications that can assist in the identification of optimised treatment strategies for patients based on their individual genetic makeup. This is a fantastic opportunity for a graduate with mathematical training to apply their knowledge in a healthcare setting and to develop highly sought after state-of-the-art skills in data analytics.

The student will be supervised by Dr Christopher Yau who leads the Statistical Machine Learning in BioHealth group (http://cwcyau.github.io). Dr Yau also co-leads “Quantitative Methods, Machine Learning and Functional Genomics” Genomics England Clinical Interpretation Partnership.

Deadline 28th May 2017.

By this date you should have checked you eligibility, applied on-line and completed the IMPACT proforma at http://www.birmingham.ac.uk/schools/mds-graduate-school/scholarships/mrc-impact/apply.aspx

For further Details please contact [Email Address Removed]

Stipend: RCUK standard rate plus travel allowance and a laptop.