Fully Funded PhD position in Machine Learning for Longitudinal Data

Studies involving animals form a controversial but necessary part of biomedical research. When we cannot completely eliminate the need for animal use, for ethical and economic reasons, we aim to reduce the number of animals in the study. In experiments where animals or litters are measured repeatedly over time, we can use longitudinal design and analysis methods to gain statistical power. However, existing methods assume that each sample provides only one, or at most a few, measured outcomes (Diggle, Heagerty, Liang and Zeger, 2002).
In this project, the student will develop statistical and machine learning models for modern high-throughput experiments, such as brain imaging or gene expression measurements, that deliver very high-dimensional outcome data from a single sample. While most research acknowledges the dependence amongst different dimensions of the data (e.g. linkage disequilibrium in the genome, spatial correlation in brain function imaging), there is a need for study design and analysis methods that bridge the gap between traditional longitudinal studies and the high-dimensional world of biomedicine. The student will apply the methods developed to mouse functional brain imaging data from the lab of our collaborator Dr Neil Dawson. The aim will be to gain new scientific insights into developmental changes in mouse brain function, and to demonstrate the effectiveness of the high-dimensional longitudinal method for increasing the statistical power and reducing the number of mice needed.
The project will be jointly supervised by Professor Peter Diggle and Dr Frank Dondelinger at the Centre for Health Informatics, Computing and Statistics (CHICAS).
More information at http://www.lancaster.ac.uk/fhm/study/phd-study/res...
Students wishing to apply should in the first instance contact Dr Frank Dondelinger (f.dondelinger-AT-lancaster.ac.uk) informally to discuss suitability. The closing date for applications is Tuesday 28th February 2017.
FUNDING NOTES
This is a three-year, fully funded position suitable for UK nationals, or EU nationals who have been resident in the UK for three years prior to application (this can include residence while undertaking undergraduate study). Funding includes a competitive stipend of £18,000/year, as well as a research training and support grant.