Senior Research Associate in High-Dimensional Longitudinal Methods

Lancaster Medical School
Salary: £34,804 to £40,322
Closing Date: Friday 29 November 2019
Interview Date: Monday 09 December 2019
Reference: A2844

The Centre for Health Informatics, Computation and Statistics at Lancaster University is recruiting a post-doctoral researcher to work on high-dimensional statistical methods for longitudinal population studies. You will join a three-year project funded by a Wellcome Trust Longitudinal Population Studies grant, and supported by academics at the universities of Sheffield, Southampton, Cambridge and Bristol.

With the advent of high-throughput genomics, proteomics and metabolomics, it has become common practice to collect and process longitudinal biological samples to follow the evolution of a disease or condition using molecular markers. These molecular markers are usually high-dimensional in nature, requiring sophisticated statistical processing. While our ability to measure high-dimensional markers has been steadily increasing, the ability to analyse these data effectively has not kept pace.

The research will bridge the gap between traditional longitudinal population studies and the high-dimensional world of molecular measurements, by developing new approaches for study design, modelling and inference that fully exploit both the cross-sectional (between dimensions) and longitudinal (between times) dependence structures of the data collected in these studies. You will work under the direct supervision of Dr Frank Dondelinger, a machine learning expert with extensive experience working on biomolecular datasets. The project team further includes leading statistical experts in longitudinal statistics (Professor Peter Diggle, Lancaster University), Gaussian processes (Dr Mauricio Alvarez, University of Sheffield) and Bayesian experimental design (Professor David Woods, University of Southampton). You will develop cutting-edge statistical and machine learning methods for dealing with high-dimensional longitudinal data. These methods will then be applied to molecular data from two large-scale population studies: metabolomic measurements from the Pregnancy Outcome Prediction Study (POPS) and DNA methylation measurements from the Avon Longitudinal Study of Parents and Children (ALSPAC).

You will be a motivated researcher with experience in developing statistical or machine learning methods and a keen interest in applying your skill set in the biomedical field to improve human health and our understanding of biological longitudinal processes.

The Centre for Health Informatics, Computation and Statistics is a vibrant and diverse research group within the Lancaster Medical School, comprising researchers into spatial and longitudinal statistics, machine learning, statistical genomics and epidemiology. The group has close ties to the School of Mathematics and Statistics and the Lancaster Data Science Institute, both of which have a reputation for excellence in statistical and computational research.

Lancaster University subscribes to the Researcher Development Concordat, and you will be fully supported in your professional and personal development. Lancaster University is committed to Equality, Diversity and Inclusion, and the Faculty of Health and Medicine holds a Silver Athena SWAN award. The Lancaster University Pre-School Centre is rated an Outstanding Provider by Ofsted and staff get preferential access to childcare places.

Informal inquiries about can be made to Dr Frank Dondelinger: f.dondelinger@lancaster.ac.uk

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