PhD Opportunities

Power cables are subject to electrical, thermal, mechanical, and environmental stresses on a constant basis when in service. These stresses, and occasionally problems resulting from inadequate installation and maintenance practices, lead to insulation degradation or defects which in turn produce, for example, excessive sheath circulating current, partial discharge activity and increasing dielectric loss, and result in premature cable failure causing unplanned outages. The aim of this project is to apply statistical models such as Weibull distribution and Proportional Hazard Model (PHM) tor analysis of cable failure and/or condition monitoring data in order to

• Establish a statistics based model for modelling of failure data and identify main influencing factors;
• Predict future failure rate using the statistics based model, financial implications will also be considered. Sensitivity analysis will be carried out to evaluate the impact of each particular maintenance or condition monitoring activities on the overall reliability and financial consequence.
• Optimise condition monitoring and maintenance activities with an aim of minimising outage time of wind generation systems, and
• Optimise the life cycle management with maintenance, condition monitoring and parts replacement to be taken into consideration.

Required background and/or experience of the candidate

The candidate should possess a relevant 1st or 2.1 honours degree to have a strong background in two or more of the following three areas:

1. Degradation process of high voltage insulation
2. Statistical modelling
3. Computer programming using Matlab or C++

It is anticipated that the candidate will liaise with industry and that he/she will have to travel nationally/internationally for meetings and conference presentations. For further project information, please contact Prof. Chengke Zhou.

How to Apply

Application forms are available on request from researchapplications@gcu.ac.uk. Please state the Project Title (above) on the completed application form and return to this email address.

Reference number: 11/12_02

Materials can have a positive effect on moderating the variation of indoor humidity in occupied buildings. This ability is known as moisture buffering. 

The overall aim of the project would be to improve our understanding of the use of materials to reduce environmental impacts due to moisture. For example, can moisture buffering save energy by reducing the requirement for ventilation? Is it possible to improve the indoor air quality by using moisture buffering to control humidity?

The objectives of this proposal are to:

• (i) develop methods to assess moisture buffering following on from a preliminary investigation by Dr. Baker
• and (ii) enhance the theoretical framework for moisture modelling of buildings. 

The work would involve:

• The design of a small-scale test system and the development of a test methodology for the measurement of moisture buffering of a range of building materials and finishes under dynamic conditions.
• Performance of full-scale tests under controlled dynamic conditions in the Environmental Chamber in SEBE. 
• Validation and development of the hygro-thermal simulation model using both small- and full-scale test data. 
• The determination of how well the steady state properties of materials used as the basis for a hygro-thermal simulation model enable the prediction of the dynamic conditions found in real buildings.
• The assessment of the benefits of moisture buffering as a strategy for controlling humidity to improve indoor air quality and energy saving.
• There will be collaboration with ESRU, University of Strathclyde on modelling aspects.

Project Lead: Dr Paul Baker  

Required background and/or experience of the candidate

The candidate should possess a relevant 1st or 2.1 honours degree. It is anticipated that the candidate will liaise with industry and that he/she will have to travel nationally/internationally for meetings and conference presentations.

How to apply

An application form is available to download (from the Research Applications section) and once completed should be returned to researchapplications@gcu.ac.uk.

Please state the Project Title and reference number (as above) on the completed application form.

Object tracking is required in many vision applications such as medical imaging, augmented reality human-computer interfaces, road traffic control surveillance systems. The goal is to obtain a record of the trajectory of moving single or multiple targets in time and space. Since many of the above applications are required to run in real-time, very low computational complexity is a highly desirable property. The primary objective of this project is to develop and implement novel fast and robust object tracking methods.

Project Lead: Dr Gordon Morison

Required background and/or experience of the candidate

The candidate should possess a relevant 1st or 2.1 honours degree. Applicants must also have strong computer programming skills (MATLAB, C/C++), excellent written and spoken English language communication skills. Experience in image processing and pattern recognition projects. Experience of working with multi-core, GPU or FPGA platforms is desirable but not essential. It is anticipated that the candidate will liaise with industry and that he/she will have to travel nationally/internationally for meetings and conference presentations.

How to apply

An application form is available to download (from the Research Applications section) and once completed should be returned to researchapplications@gcu.ac.uk. Please state the Project Title on the completed application form.

Reference number: 11/12_14

The demand for understanding of digital forensic methods continues to increase as criminals exploit rapid technological advances to achieve unintended opportunities in the commission of crime, and employ ingenious methods to invalidate evidence. However, devices can inadvertently serve as silent witnesses to user activity, by storing information about user interactions.

The popularity of iOS devices is causing challenges for law enforcement, including those related to the effects of natural data movement in push-based cloud formations. Understanding basic device functionality (e.g., what and how data is stored and modified) and being able to explain how data originated is vital, especially as data movement can have a profound impact on the validity of data.

The project will address challenges such as uncontrolled data transfer, data contamination, and the unintended prospect for obstructing and obscuring investigations for law enforcement and corporate forensic examiners. It aims to establish a practical understanding of data on iOS devices, the forensic implications of data deletion, and whether it is possible to forensically recover iOS based data by utilising innovative techniques, particularly related to the cloud. This project will allow the candidate to be part of a large Scottish digital forensics community, and will include collaboration with the Scottish Crime and Drug Enforcement Agency (SCDEA).

Project Lead: Dr Michelle Govan  

Required background and/or experience of the candidate

The candidate should possess a relevant 1st or 2.1 honours degree. It is anticipated that the candidate will liaise with industry and that he/she will have to travel nationally/internationally for meetings and conference presentations.

How to apply

An application form is available to download and once completed should be returned to researchapplications@gcu.ac.uk Please state the Project Title and reference number (as above) on the completed application form.

• A long and established history within education and research
• A significant RAE 2008 score
• Staff who are widely recognised within their discipline
• Accreditation for our degree programmes from a range of leading professional bodies
• Strong partnerships with other universities, colleges and training organisations, both nationally and internationally
• Excellent professional links with a wide range of employers and local authorities
• High graduate employability rates
• Excellent teaching and learning facilities
• Flexible and relevant research that is designed for a wide range of professionals
• A range of flexible approaches to your study mode including full-time on campus, part-time off campus, self-funded and university/industry funded