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.

Network design for space weather monitoring (PhD)

Supervisors: Peter Craigmile
Relevant research groups: Environmental Statistics, Statistical Modelling

A PhD opportunity for working on the statistical modelling and design of a sensor network for space weather monitoring is available as part of a new interdisciplinary project on space weather monitoring.

Space weather is the name given to electromagnetic disturbances in the solar system and the near-Earth environment. It can damage spacecraft and disrupt electrical power networks and GPS. The project offered will focus on developing statistical designs of a sensor network of small satellites moving through space, and measuring space weather conditions, including the magnetic field, semi-continuously.

The project is suitable for a student with a strong background in statistics or mathematics, and an interest in space science and data analysis. Candidates should have an excellent single or combined degree in mathematics, statistics or physics and have strong computational skills (for example, using the R language). 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 system. This exciting project will be co-supervised by academics from the Schools of Physics and Astronomy, Mathematics and Statistics, and Engineering.

The stipend is based on the UK Research Council rates and the studentship will be of 3.5 years duration.

For further information, please contact Dr. Peter Craigmile (peter.craigmile@glasgow.ac.uk)

Deadline for applications: 1 May 2013.
Start date: 1 October 2013.

Interactive representations of uncertainty for modern statistical inference (PhD)

Supervisors: Peter Craigmile
Relevant research groups: Statistical Modelling, Statistical Methodology

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)

Health & Educational Impacts of Socio-Ethnic Migration & Neighbourhood Dynamics in Scotland (PhD)

Supervisors: Peter Craigmile
Relevant research groups: Statistical Modelling

Summary

The ethnic/religious composition of our cities and neighbourhoods is diverse and rapidly changing, with profound implications for social justice and cohesion. Current immigration policy, for example, with its far-reaching impacts on education and employment, is underpinned by a particular set of perceptions about ethnicity and the long-term effect of assimilation. Such policies are set against a backdrop of sociological concerns about how society perceives and integrates immigrant communities, and how contours of disadvantage fall along racial and religious lines, and persist down generations. For example, Devine et al. (2000) argue that discrimination towards early-twentieth-century Catholic immigrants became embedded, to the extent that these socio-religious divisions remain a source of disadvantage.
Much of this debate, however, is based on anecdotal evidence, small-sample/short-run case studies, or on area/group averages which are vulnerable to the Ecological Fallacy (using group-level results to make false inferences about individuals within those groups). Migration flows of individuals from particular ethnic/social backgrounds remain poorly understood at micro-spatial-scales and there is very little reliable evidence on the implications for long-term wellbeing.
This project will achieve a step-change in the scientific rigour applied to ethnicity research. Applying cutting-edge quantitative techniques to a unique combination of high-quality Scottish datasets, we shall open-up new avenues for ethnicity/inequality-research, and develop novel social-statistical methods that will have applications in other contexts. We shall follow 270,000 individuals over a twenty-year period, linking the greatly-underutilised Scottish Longitudinal Study (SLS) to a rich combination of data on neighbourhood composition, health, house prices and education. This will allow us to map-out the individual life-trajectories and health outcomes of persons identified as Catholic in 1991, compare these trajectories with those of persons from different ethnic/religious backgrounds, and shed light on questions about the extent to which long-term discrepancies in social mobility and health outcomes are driven by ethnicity/religion and/or neighbourhood effects.

Dates

The studentship is available for the period: October 2013 to Sept 2017

Scholarship Details

Each scholarship includes a student stipend of £13,726 per annum, plus a fee waiver, plus £5,300 per annum for conferences attendance and research costs. The student will be based in Urban Studies, University of Glasgow, and be linked to the ESRC LDOES project (Location Dynamics, Owner-occupation and Ethnicity in Scotland).

Supervisors

• Lead supervisor: Gwilym Pryce (Urban Studies)
• Co-supervisors: Dr Peter Craigmilie (Statistics) and Dr Andy Smith (Sociology)

For more information:

Contact Professor Gwilym Pryce (gwilym.pryce@glasgow.ac.uk) for more details.

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.

Show all past events relating to this group