Multimedia & Vision Vacancies

Current Positions

• Two Phd Scholarships on AI models for automated understanding of visual data in the broadcasting industry

  
  Expected start date: September 2019
  
  Applications are invited for two fully funded PhD positions on AI models for automated understanding of visual data in the broadcasting industry.
  
  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. Two additional highly motivated PhD students are sought to complement this joint academia/industry research group.
  
  How to apply
  The studentships will be based in the School of Electronic Engineering and Computer Science (EECS) at Queen Mary University of London, in the Multimedia and Vision Research Group (MMV). However, successful candidates will also spend half of their PhD research time working in the research labs of the BBC at White City, London. Consequently, these research studentships will give successful candidates the opportunity to gain valuable practical experience in solving problems and improving systems developed for and used by the broadcasting sector.
  
  The projects undertaken under these studentships are also expected to fit into the wider research vision of the MMV group within the scope of the BBC/QMUL Data Science partnership
  
  These studentships are for 3 years each and cover student fees and a tax-free stipend starting at £16,777 per annum. 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. These scholarships are only available to UK nationals and candidates with UK resident status.
  
  To apply please follow the on-line process (see QMUL: How to apply for postgraduate programmes ) 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 ebroul.izquierdo@qmul.ac.uk and Dr. Marta Mrak at Marta.Mrak@bbc.co.uk.

Past Positions:

• School of Electronic Engineering and Computer Science
  PhD scholarship on Computer based histopathological image analysis for cancer grading and progression assessment

  
  Expected start date: June 2018
  
  Supervisor: Dr Qianni Zhang (qianni.zhang@qmul.ac.uk)
  
  Applications are invited for this fully funded PhD position on computer based histopathological image analysis for cancer grading and progression assessment. The assessment of pathological cancer regression after preoperative chemotherapy is mostly based on the assessment of tumour morphological features, such as the proportion of cancer cells in relation to the total tumour region, as well as biologically relevant histology features, such the tumour invasion front. Currently, this histopathological evaluation is performed by expert pathologists through visual assessment of the tumour microscopic slides. This is often time-consuming, expensive and may be unacceptably inconsistent and imprecise. This project aims at developing an intelligent system that enables automatic, precise, objective and reproducible assessment of tumour regression and precise characterisation of the tumour invasion front based on the digital scans of resected tumour tissue slides, by integrating beyond the state-of-the-art, specifically designed computer vision, image processing and machine learning schemes.
  
  How to apply
  The studentship will be based in the School of Electronic Engineering and Computer Science (EECS) www.eecs.qmul.ac.uk at Queen Mary University of London, in the Multimedia and Vision Research Group. The project undertaken under this studentship is expected to fit into the wider research programme of the group, which has widespread recognition for its research in image processing and computer vision areas.
  
  This studentship, funded by the School of Electronic Engineering and Computer Science, is for 3 years and will cover student fees and a tax-free stipend starting at £15,600 per annum. Candidates should have a first class honours degree or equivalent, or a strong Masters Degree, in computer science, mathematics, or electronic engineering.
  
  To apply please follow the on-line process (see QMUL: How to apply for postgraduate programmes ) 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.
  
  Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your Statement of Research Interest should answer two questions: (i) Why are you interested in the proposed area? (ii) What is your experience in the proposed area? Your statement should be brief: no more than 500 words or one side of A4 paper. Mark clearly the statement with Your Name and the title “semantic image understanding and 3D reconstruction”. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php

• School of Electronic Engineering and Computer Science
  PhD scholarship on semantic based media synchronisation, aggregation and event plotting

  
  Applications are invited for this fully funded PhD position on semantic based media synchronisation, aggregation and event plotting. The project focuses on the automatic understanding of semantic objects and events in videos and combining the content from different sources into a single plot to produce highly appealing media for describing an event.
  
  This studentship, funded by EU Horizon 2020 project COGNITUS (Converging Broadcast and User Generated Content for Interactive Ultra-High Definition Services), is for 3 years and will cover student fees and a tax-free stipend starting in the range of £15,600 per annum plus a travel fund for attending collaborative meetings and international conferences. Candidates should have a First Class Honours or a good 2.1 degree in Mathematics, Electronic Engineering, Physics or Computer Science and have excellent computer programming skills. Background in image or video processing and computer vision is desired.
  
  Informal enquiries should be addressed to Dr Qianni Zhang (qianni.zhang@qmul.ac.uk)
  
  To apply please follow the on-line process (see QMUL: How to apply for postgraduate programmes ) 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.
  
  Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your Statement of Research Interest should answer two questions: (i) Why are you interested in the proposed area? (ii) What is your experience in the proposed area? Your statement should be brief: no more than 500 words or one side of A4 paper. Mark clearly the statement with Your Name and the title “semantic based media synchronisation, aggregation and event plotting”. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: QMUL EECS: How to apply for a PhD

• PhD (UK Nationals only)

  
  Applications are invited for a fully funded PhD studentship on video compression with specific focus on the emerging HEVC standard.
  
  The aim of this studentship is to study and optimize motion compensation techniques for advanced video coding. The studentship involves a close collaboration with The BBC and the practical outcomes of the research work are expected to influence emerging video coding standards.
  
  Candidates should have a First Class Honours or a good 2.1 degree in Mathematics, Electronic Engineering, Physics or Computer Science and have excellent computer programming skills. Experience with image or video coding techniques is essential. A background in image or video processing is also required.
  
  Informal enquiries should be addressed to Prof. Ebroul Izquierdo at ebroul.izquierdo@eecs.qmul.ac.uk.
  
  Details about the school can be found at www.eecs.qmul.ac.uk
  
  Funding Notes:
  
  The grant will cover all study fees for UK nationals/residents and a living stipend for three years. The stipend is in the range £15,600 per annum plus a travel fund for attending collaborative meetings and international conferences. Non-UK nationals/residents are not eligible for this studentship.

• PhD

  
  Applications are invited for a fully funded PhD studentship on large scale analysis of forensic data for event-based representation and discovery. Specifically, the successful candidate will conduct R&D on knowledge representation, reasoning to quantify uncertainty through fuzzy analysis, extraction of data patterns and anomalies and knowledge visualization to assist forensic analysis.
  
  Candidates should have a First Class Honours or a good 2.1 degree in Mathematics, Electronic Engineering, Physics or Computer Science and have excellent computer programming skills. Experience with image or video processing techniques is also desirable.
  
  Informal enquiries should be addressed to Prof. Ebroul Izquierdo at ebroul.izquierdo@eecs.qmul.ac.uk.
  
  Details about the school can be found at www.eecs.qmul.ac.uk