Topics in compositional data analysis (MSc)

Supervisors: Tereza Neocleous
Relevant research groups: Statistical Modelling

Modelling data from brain images (MSc / PhD)

Supervisors: Claire Miller (née Ferguson), Adrian Bowman
Relevant research groups: Statistical Modelling

There is a collaboration underway with the Department of Psychology, who have a full suite of brain imaging equipment.  The current focus is on MEG data, where subjects wear a helmet with embedded electrodes and these pick up signals from the brain over time.  The data are high resolution and quite complex.  The challenge is to use statistical methods to identify the signal from the considerable noise which is present in these experiments.  Current work centres on the use of smoothing techniques to do this.  However, there is very considerable scope for PhD (and MSc) projects in this topic.  There is strong support and interest from the Department of Psychology.

Modelling three-dimensional shape data (MSc / PhD)

Supervisors: Adrian Bowman
Relevant research groups: Statistical Modelling

Modern imaging equipment provides very interesting forms of data.  This project focusses on a stereo camera system which is able to construct a three-dimensional model of the surface of an object.  There are many clinical applications of this.  A longstanding one is in the analysis of facial shape of those who have undergone surgery for conditions such as cleft lip and palate.  More recently attention has shifted to quantifying the effects of surgical operations in adult faces.  Analysis of the data raises very interesting questions about how to measure shape and shape change.  One research students and one research assistant are already working on these topics but there are quite a few possibilities for further student projects, as part of this small team.

Spatiotemporal modelling of hydrological catchments (PhD)

Supervisors: Claire Miller (née Ferguson), Marian Scott OBE, Adrian Bowman
Relevant research groups: Environmental Statistics, Statistical Modelling

Regulatory bodies, such as the Environment Agency of England and Wales and the Scottish Environment Protection Agency, regularly monitor river surface water. The purpose of this is to assess the levels of nutrients etc. in the water to report levels to Europe and also to address how to reduce such levels, if necessary, to meet European standards.

River monitoring locations are contained in small waterbodies, which are the standard surface water reporting units for the Water Framework Directive (WFD, European Parliament; 2000). A collection of waterbodies, covering a river network, are contained in a large hydrological area (LHA), and each LHA contains an independent river network. This project will develop spatiotemporal hierarchical models for such data incorporating different levels of spatial correlation within and between catchments and contributory catchment information.

There are several statistical challenges associated with such modelling including incorporating space-time interactions and space-time covariance structures. The overall data dimensionality for such problems is large. However, at particular monitoring locations data can be sparse in space and/or time. Monitoring locations along rivers are flow-connected with models requiring catchment information and river network covariance structures.

Interactive representations of uncertainty for modern statistical inference (PhD)

Relevant research groups: Statistical Modelling, Computational Statistics and Inference

In many scientific areas the exploration of the uncertainty of parameters or quantities of interest is as important as estimating the individual effects. A careful assessment of uncertainty using statistical modelling lends support to a scientific analysis. But such assessments can be limited by the use of static summaries for data and statistical model parameters, which cannot capture the space of variations or the sensitivity of model elements to change. Interactive exploration of uncertainty, while more computationally intensive, can afford investigation of otherwise difficult-to-perceive interactions and relationships between quantities of interest, and can generate new scientific hypotheses. This Ph.D. project will propose and investigate methods for effective interactive exploration of uncertainty in data and statistical model output suited to large sensor networks.

Candidates should have an excellent single or combined degree in Statistics or Computing Science and have strong computational and mathematical modelling skills. Excellent written and oral communication skills, and strong time-management skills are also desirable.

The College of Science and Engineering at the University of Glasgow is investing in interdisciplinary research on sensors and sensor systems. This exciting project will be co-supervised by academics from the Schools of Computing Science, and Mathematics and Statistics.

The studentship covers fees and a stipend (at UK and EU student levels). The stipend is based on the UK Research Council rates and the studentship will be of 3.5 years duration.

Closing date: Monday 20th May 2013
Expected start date: 1st October 2013

For further information, please contact
Dr. Peter Craigmile (peter.craigmile@glasgow.ac.uk) or
Dr. John Williamson (jhw@dcs.gla.ac.uk)

Detection of genomic signals in sequence data (PhD)

Supervisors: Mayetri Gupta
Relevant research groups: Biostatistics and Statistical Genetics, Statistical Modelling

Demarcating functional regions in the genome is an essential component in gaining insight into the working of biological systems, from the cellular level to the organism as a whole. One important problem is the accurate detection of transcription factor binding sites, which act as "switches" turning genes on or off as needed. Detection of these sparse signals from genomic sequence data is a significant challenge, due to the high volume of noise compared to the actual signal, along with latent dependencies in the data such as positional or structural constraints. Augmenting sequence data with additional information- such as knowledge of biological pathways, or transcriptional experiments, presents a more powerful alternative, but leads to additional challenges in statistical modelling and analysis. In this project, we aim to develop fast, accurate and efficient statistical methods for the detection of transcription factor binding sites. Techniques that will be used may involve hidden Markov models for segmentation, joint modelling approaches, and fast and efficient Markov chain Monte Carlo-based computational techniques for model-fitting and estimation. Using a Bayesian statistical framework in such problems is highly desirable and appropriate, in order to bring in a necessary structure to the problem, and incorporate pertinent biological information that can lead to more accurate inference. A related problem to be addressed, with somewhat different challenges, is detecting boundaries of functionally varying genomic regions, such as nucleosomes, or regions of methylation.

Universal laws of mass migration: From cancer cells to wildebeest (PhD)

Supervisors: Dirk Husmeier
Relevant research groups: Statistical Modelling

A fully funded PhD position is available for interdisciplinary work on universal laws of mass migration.

The project will be supervised by Jason Matthiopoulos, Dirk Husmeier, Robert Insall and Grant Hopcraft.

The project would suit a numerate biologist or a physical/mathematical scientist with a keen interest in cellular or ecological problems.

More information can be found in the PhD advert.

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