|Funding for:||UK Students, EU Students|
|Funding amount:||Tuition fees + stipend available for Home students or EU students who have been resident in the UK for 3 years|
|Placed On:||19th June 2019|
|Closes:||31st August 2019|
Principal Supervisor: Prof Sotirios Tsaftaris (University of Edinburgh) S.Tsaftaris@ed.ac.uk
Assistant Supervisor: Dr Joao Mota (Heriot-Watt University) firstname.lastname@example.org
Data nowadays are created at a staggering pace. This poses diverse challenges in terms of storage and transmission of large quantities of such multimedia data. Despite advances in compression algorithms, these rely on universal principles and, therefore, do not easily adapt to data. In the last few years, however, major breakthroughs in compression rates have been achieved with deep neural networks due to their ability to extract underlying structures from large quantities of data. While this allows state-of-the-art compression rates, it also makes the compression algorithms less robust to interference and information loss.
The goal of this PhD project is to create algorithms that are both compression efficient and robust to interference whilst being computationally efficient. This will be done by exploring how state-of-the-art neural network architectures can be combined with recent sparse inference algorithms, and also with classic compression techniques.
The project will be supervised by Prof. Sotirios Tsaftaris, from the University of Edinburgh, and Dr. Joao Mota, from Heriot-Watt University.
The student will work on the University Defence Research Collaboration (UDRC) (www.mod-udrc.org), which is a leading research partnership for signal processing for defence and develops new techniques to better transform data across many domains into actionable information, and meet the requirements for improved situational awareness, information superiority, and autonomy. This collaboration, sponsored by Dstl and the EPSRC, is academia-led and has commenced its third phase of research focusing on "Signal Processing in the Information Age". The Consortium is made up of the University of Edinburgh, Heriot-Watt University, Queen's University Belfast and University of Strathclyde and there are currently PhD opportunities available across the four universities to work on diverse topics in signal processing, as part of a collaborative team. The work will involve strong links with industry and the UK defence sector as is part funded by an industry partner. The PhD student will be expected to work closely with other research team members and to attend regular meetings to present project updates to the sponsors and partners of this project.
Candidates should have completed, or expect to complete, an MSc degree in Electrical Engineering, Mathematics, Computer Science, or equivalent. Candidates should have a good background in mathematics, an autonomous and proactive working style, and good communication skills. Familiarity with machine learning and/or optimization algorithms is a big plus.
EPSRC funded (see EPSRC student eligibility). Tuition fees + stipend available for
Home students or EU students who have been resident in the UK for 3 years (International students not eligible)
Undergraduate degree in Electrical Engineering, Computer Science, Mathematics, or related area.
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