The Distance Learning PhD in Electronic and Electrical Engineering has the same level of challenge as the on-site PhD study. The difference is that the research topic must be set in the way that the research is done remotely. The remote postgraduate research assumes that no work can be done in person in the laboratory including fabrication, characterisation, and experiment set-up. If the research topic involves fabrication, characterisation, and experiment set-up, the PhD study must be conducted in-person in order to be physically present in the laboratory or the cleanroom. The remote postgraduate research is likely to be using commercial software tools, open-source software, or in-house developed software, even other remotely performed research work can be envisaged depending on the ideas of a supervisor. Some practical design and test work can be done at home if the parts can be purchased online or delivered by post.
The Distance Learning PhD in Electronic and Electrical Engineering has these requirements for distance learning PhD students:
- A strict attendance of regular meetings with your supervisor - minimum once a month
- Remote participation in the research-related activities at the faculty and at the school, including regular monthly PhD research seminars and the university/faculty/school/department organised research events
- Demonstration of a solid internet connection for the duration of PhD study
- Operating post or delivery service at the location of the PhD study
Start dates: PhD/MPhil - 1st October, 1st January, 1st April & 1st July.
As a world-leader in the research areas of power semiconductor technology and devices, power electronics, nanotechnology and biometrics, and advanced numerical modelling of micro and nanoelectronic devices, Swansea University provides an excellent base for your research as a PhD or MPhil student in Electronic and Electrical Engineering.
The PhD in Electronic and Electrical Engineering have expertise in a broad range of topics.
See our Research Expertise.
Previous projects have included:
- Molecular dynamics simulations of nanoclusters in neuromorphic systems
- Forecasting and Prediction of Solar Energy Generation using Machine Learning Techniques
- Design and Implementation of Control Techniques of Power Electronic Interfaces for Photovoltaic Power Systems
- Design of Ancillary Services for Battery Energy Storage Systems to Mitigate Voltage Unbalance in Power Distribution Networks
- Parallel 3D Finite Element Monte Carlo Device Simulations Of Multigate Transistors
- Modelling of Metal-Semiconductor Contacts for the Next Generation of Nanoscale Transistors
- Novel GaN HEMT Switches for Power Management: Device Design, Optimization and Reliability Issues