KTP Associate in Video Scene Understanding and Inpainting - Supponor Ltd Salary:£38,000 to £40,000 per year Closing date:22 June 2021
This role is a unique opportunity to work with Oxford Brookes specialists to utilise machine learning to achieve real-time understanding of video scenes and consistent segmentation of advertisement boards and sports pitch objects without the use of existing infrared cameras and hardware infrastructure. You will be employed by the university and supervised by Prof. Fabio Cuzzolin of the School of Engineering, Computing and Maths. Your place of work will be Supponor’s offices in Hammersmith where you will lead a KTP project funded by Innovate UK. This is a full time, fixed term post for 3 years, subject to funding. About Supponor Limited: Supponor is the leading provider of real-time, in game, virtual advertising solutions to the sports industry. Our platform is used in the worlds top tier sports leagues to provide authentic looking virtual replacement of physical in-stadia advertising signage, allowing brands to better target different audiences during live broadcasts. Learn more at www.supponor.com. About Oxford Brookes University Oxford Brookes is one of the UK’s leading modern universities and enjoys an international reputation for teaching excellence and innovation as well as strong links with business and industry. The successful candidate must have: A degree and relevant experience as detailed in the full job specification on our website. A desire to learn new skills and undergo job-related training is also important. Qualifications we require: A minimum Master’s degree in Computer Science or a related discipline. A PhD in machine learning, computer vision or related fields is desirable. Essential skills we require as a minimum: A good level of deep learning and machine learning skills, a high level of skill with Python and experience with Torch or PyTorch. If you would like an informal discussion about the role please contact Professor Fabio Cuzzolin at fabio.cuzzo...@brookes.ac.uk. To apply please follow: https://findajob.dwp.gov.uk/apply/5878507
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