Fixed-term: The funds for this post are available for 3.5.
Applications are invited for a PhD studentship in video quality. The goal is to develop algorithms for the automated assessment of video quality, which could be used to optimize content delivery. The algorithms will combine machine learning techniques with models of displays and human vision to predict quality with respect to a given display (resolution, size, peak luminance) and viewing conditions (viewing distance, ambient light). The metrics will work with both standard and high dynamic range (HDR) content. The goal is to combine the expertise from human vision research with modern machine learning techniques, to create robust and explainable metrics of video quality. The results of this work can potentially change video delivery and optimize video processing for millions of streaming video users worldwide.
The successful candidate will be encouraged to work with the project partner, Netflix, including taking part in summer internships.
The results of the work will be made freely available as open-source projects.
The successful candidate will start the PhD programme in October 2022.
Our group consists of PostDocs and PhD students investigating ways in which computer vision, computer graphics and signal processing can be improved by incorporating the models and knowledge of human vision. The group consistently publishes their work at premium computer graphics and vision venues, such as SIGGRAPH, CVPR, ECCV, IEEE TIP. The position is within the Graphics & Interaction Group (https://www.cl.cam.ac.uk/research/rainbow/) of Cambridge Computer Laboratory, a vibrant and internationally leading environment. Collaboration with researchers at other universities and industries around the world is encouraged and there are strong links within the group with local, national and international companies.
We seek candidates with a strong background in Computer Science and/or Image Processing (1st class honours degree or equivalent, although a Master’s is particularly desirable) with a particular interest in colour, image processing and machine learning. The successful candidate will need to work with large quantities of data and must poses excellent programming and software engineering skills. It is desirable that the candidate is familiar with PyTorch, Tensorflow or similar machine learning frameworks.
Candidates need to meet all prerequisites for admission to the PhD in Computer Science (please refer to: https://www.cst.cam.ac.uk/admissions/phd).
This position is open to applicants from anywhere in the world; all university fees will be paid by the project and the successful candidate will receive a stipend at the UKRI rate (https://www.ukri.org/our-work/developing-people-and-skills/find-studentships-and-doctoral-training/get-a-studentship-to-fund-your-doctorate/), currently £15,609 per year.
Further details may be obtained from https://www.cl.cam.ac.uk/~rkm38/, email firstname.lastname@example.org
Complete applications, including two academic references, research proposal, transcripts and degree certificates, CV and a cover letter should be submitted via the Applicant Portal by the application deadline, see https://www.graduate.study.cam.ac.uk/how-do-i-apply. Please provide a Curriculum Vitae (CV) and a covering letter outlining your relevant past experience, drawing particular attention to relevant software experience and linking to one or more examples of code written (e.g. a GitHub handle). Queries regarding the application process should be directed to email@example.com.
Students wishing to pursue a PhD at the University of Cambridge are required to submit a short research proposal outlining the work they intend to carry out during the PhD. Candidates should get in touch with https://www.cl.cam.ac.uk/~rkm38/ to discuss this before applying, outlining their ideas for initial research directions.
Please quote reference NR29600 on your application and in any correspondence about this vacancy.
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
The University has a responsibility to ensure that all employees are eligible to live and work in the UK.