Novel Machine Learning for Object Recognition and Scene Understanding

Applications are invited for a PhD studentship which aims to develop a novel machine learning approach for object recognition and scene understanding. The studentship is part of a collaborative project led by Brunel University London and the National Physical Laboratory (NPL). Brunel University London is supporting the development of the interdisciplinary research Institute of Innovative Quality Engineering and Smart Technology (I2QUEST). The I2QUEST is an international Industry - University - Research innovation platform, recently launched with Sichuan Mingxin Investment Group (SMIG), supported by Chengdu High-tech Zone, with its vision to be a world leading Centre of Excellence in innovative quality engineering and smart technology.

Object recognition and scene understanding are essential abilities for robots and autonomous systems to navigate in its environment and achieve their operational goals. Depp learning has been applied in object recognition and scene understanding with impressive performance bit it generally requires a very large amount of data and expensive computations with the learned knowledge difficult to comprehend.

This studentship will develop a novel machine learning approach to overcome the limitations of common deep learning, whilst matching its learning power and performance. A sound methodology will be developed and validated with experiments and demonstrators.
The student will be based at Brunel’s Uxbridge campus and will be part of the research team in the College of Engineering Design and Physical Sciences. The student will be able participate in national and international conferences, interact with industrial partners, and will have an opportunity to travel to Chengdu, China, to work with collaborators associated with the Chengdu High-Tech Zone.

For an informal general discussion regarding the above post, please contact Dr QingPing Yang at [Email Address Removed].