Projects in the area of computational science and engineering (Scientific visualization)
- Self-funded PhD students only
- Number of awards: Multiple
- Deadline: Ongoing.
- Supervisors: Contact Dr Hamish Carr to discuss any of these projects further.
There are many potential projects for PhD research within the group. On this page we outline some of these projects. This list is by no means definitive and you should feel free to contact Dr Hamish Carr to dicuss.
2D Fiber Surfaces
Recent work has generalised isosurfaces for scalar field visualisation to fiber surfaces for bi-field visualisation in volumetric data. However, much data, especially from medical acquisition, is commonly analysed in image form. The goal of this PhD is therefore to apply the 2D analogue of fiber surfaces to image analysis, and to compare it with existing methodologies for image-based boundary detection.
Reduced High-Dimensional Fiber Surfaces
For analysis of multiple fields over a single spatial domain, one set of techniques is based on collapsing the individual fields into a single summary field to which scalar field visualisation can be applied. Since the recent introduction of fiber surfaces for bifield visualisation, it becomes feasible to reduce to two fields, not one, thereby preserving more variation in the data. The goal of this PhD is therefore to use this line of attack to deliver further insight into complex high-dimensional simulations.
Parallel Infrastructure for Computational Topology
Scientific simulations now routinely generate data streams vastly in excess of the ability of humans to interpret. Recent work in data analysis and visualisation has therefore applied topological analysis to the task of feature detection and interpretation. However, most of the algorithms are essentially serial, although some work has been performed on parallelisation. The goal of this PhD would be to identify common patterns across multiple topological computations, and identify how to parallelise not only one single approach, but as many as possible, by exploiting fundamental properties both of the algorithms and of the underlying mathematics that they encode.
Data-Parallel Data Structures
As a result of increased core count both on Intel Xeon Phi and nVidia Tesla platforms, global interest in SMP (shared memory parallelism) and SIMD (single instruction multiple data) computations has rekindled. For complex computations, however, development of data structures and algorithms has lagged behind serial methods. The goal of this PhD is therefore to build systematically towards data structures, tools and libraries that effectively exploit the massive parallelism now available.
Feature Detection & Analysis in Scientific Data
As data sizes scale, feature detection & analysis are increasingly important aspects of scientific computation. However, these approaches are typically developed separately, with little or no interchange between topological methods, machine learning and other forms of analysis. The goal of this PhD would be to analyse the sets of approaches to this task, establish ontologies, articulate the space of possible feature detection methods, and deliver and implement innovative new and/or hybrid feature detection methods.
Representing & Visualizing Parameter Spaces
Many scientific and engineering simulations are parameter-dependent, with 10 or more input and output parameters. Recent work has shown that visualisation of these parameter spaces is not fundamentally impossible, but requires detailed work on mathematical foundations, computational representations and visualisations. The goal of this PhD is therefore do build a rigorous framework for the analysis and visualisation of parameter spaces in general, with at least one specific problem addressed using the framework.
We would normally expect applicants for postgraduate research to have achieved one of the following in a subject relevant to the proposed research:
• a minimum of a UK upper second class honours degree (2.1), or equivalent, or
• a good performance in a Masters level course.
We also recognise relevant industrial and academic experience and special circumstances in the consideration of applicants.
How to apply
Formal applications for research degree study should be made online through the university's website.
If you require any further information please contact the Graduate School Office
e: email@example.com, t: +44 (0)113 343 8000.