Financial Information: Self-funded
Duration: 3 years
Location: Northumbria University, Newcastle upon Tyne
Department: Computer and Information Sciences
Starting Date: Flexible
We are seeking a motivated person interested in a self-funded doctoral study PhD degree. With the training provided, the PhD will research, implement solutions, and produce research papers in the exciting areas of computer vision with deep learning and multiple drones. He/she has access £124,000 worth of equipment to carry out these goals. Extra support will be provided by the supervisor and other PhDs in the team.
Taking advantages of the recent advancements in deep learning and drone systems, new applications and breakthroughs in computer vision have become possible. Areas such as surveillance, recognition, crowd analysis and 3D reconstruction, have shown significant improvements in recent years. In this project, the PhD will research on state-of-the-art deep learning algorithms. With multiple video input simultaneously, vision-based methods are to be developed to understand and analyse the content. We are interested in all sort of computer vision problems, and are particularly interested in utilizing the multi-drones systems to analyze the flow of people/vehicle movement, as well as to reconstruct the 3D shape of the people from multiple 2D view input.
The PhD will be supervised by Dr. Hubert Shum, who is the Director of Research and Innovation in the Department. He organized international conferences such as BMVC and ACM SIGGRAPH MIG, and is an Associate Editor of CGF and a Guest Editor of IJCV.
The job is based in the Department of Computer and Information Sciences, located in a new £7m purposefully designed building. It is placed by the Times Higher Education World Ranking 2019 in the top 250-300 for computer science, and is one of the largest computer science departments in the UK.
Please follow the link here. If you wish to have an informal discussion, please contact Dr. Hubert Shum by email: hubert.shum[at]northumbria.ac.uk
Last update: 26/01/2020