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AI models for automated understanding of visual data in the broadcasting industry


Project Description

The broadcasting industry is becoming increasingly reliant on automated understanding and interpretation of visual data. AI is instrumental to achieve effective and efficient systems for automated understanding of data captured with optical sensors/cameras. In this context, AI is critical to improve presentation interfaces, enhance visual quality, produce highly efficient data compression tools and achieve better computational creativity, guided by real application needs from the broadcasting sector. Whilst AI developments in the last decade has been rapid, for AI systems to be able to fully meet to the needs of the broadcasting industry in years to come, progress beyond existing technology is required. The aim is to produce visual analytics tools able to handle and understand the content in vast amounts of visual data.

QMUL has created in partnership with the BBC a team of researchers working on the development of better AI for advanced broadcasting application. This research effort is part of the recently launched BBC/QML Data Science partnership. An additional highly motivated PhD student is sought to complement this joint academia/industry research group.

How to apply

The studentship will be based in the School of Electronic Engineering and Computer Science (EECS) http://www.eecs.qmul.ac.uk at Queen Mary University of London, in the Multimedia and Vision Research Group (MMV). However, the successful candidate will also spend half of their PhD research time working in the research labs of the BBC at White City, London. Consequently, this research studentship will give the successful candidate the opportunity to gain valuable practical experience in solving problems and improving systems developed for and used by the broadcasting sector.

The project undertaken under this studentship is also expected to fit into the wider research vision of the MMV group within the scope of the BBC/QMUL Data Science partnership.

Candidates should have a first class honours degree or equivalent, or a strong Masters Degree, in computer science, mathematics, physics, electronic engineering or any other related field. This scholarship is only available to UK nationals and candidates with UK resident status.

To apply please follow the on-line process (see http://www.qmul.ac.uk/postgraduate/apply ) by selecting “Electronic Engineering ” in the “A-Z list of research opportunities” and following the instructions on the right hand side of the web page. Informal inquires can be directed to Prof. Ebroul Izquierdo at and Dr. Marta Mrak at .

Funding Notes

This studentship is for three years and covers student fees and a tax-free stipend starting at £16777 per annum.

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