Job details
Dementia is the biggest health challenge of our century. To date, there is no way to prevent it or even slow its progression, and there is an urgent need to fill the knowledge gap in our basic understanding of the diseases that cause it. The UK Dementia Research Institute (UK DRI) is the biggest UK initiative driving forward research to fill this gap.
The UK DRI at Imperial College London brings novel, cutting edge approaches and research tools to the study of dementia to discover new disease mechanisms and therapeutic targets. The team focuses on the earliest pre-symptomatic stages of Alzheimer’s disease, addressing what determines the transition to neurodegeneration, and will investigate mechanisms that are likely shared across many of the dementias.
Dr Nathan Skene programme is focused on identifying the cell types, time points and regulatory mechanisms acted on by genetic variants associated with neurodegenerative diseases. The lab develops statistical methods to integrate single-cell genomic data with genome-wide datasets on the genetic associations with brain disorders.
This PhD project is focused on using machine learning techniques to develop novel classifiers for predicting how changes in DNA sequences alter genomic regulatory features. Many regulatory proteins recognise particular DNA sequences known as motifs, for instance, EcoRI only binds to GAATTC. DNA sequences can be converted into a machine-interpretable format, using one-hot encoding.
The candidate will use publicly available and inhouse datasets of genomic regulatory features to train models. Machine learning techniques will be used to predict the cell-type specific regulatory effects of genetic variants. We will provide several true-positive datasets, wherein the effect of genetic mutations on particular regulatory features has been measured. These will form validation datasets to evaluate how well the trained classifier works.
We are interested in how improvements in the machine learning approach (e.g. use of transfer learning, recurrent attentional networks or graph convolution networks) can be used to improve upon existing methods. The candidate will use these techniques to identify causal pathways and candidate drug targets for neurodegenerative diseases.
Requirements
Applicants must hold (or obtain by October 2020) a First Class or an Upper Second Class degree (or equivalent overseas qualification) in a quantitative discipline, such as mathematics, statistics, computer science or engineering. Imperial would normally expect successful applicants to hold or achieve a Master's degree in a related field. Prior experience with programming is essential, but no experience with biology is necessary. Experience using machine learning methods will be beneficial.
How to apply
Contact details
Dr Nathan Skene: n.skene@imperial.ac.uk
Applications will be considered until November 2020.
