Funding

Self-funded

Project code

COMP6511025

Department

School of Computing

Start dates

October, February and April

Application deadline

Applications accepted all year round

Applications are invited for a self-funded, 3-year full-time or 6-year part time PhD project.

The PhD will be based in the School of Computing and will be supervised by Professor Zhaojie Ju.

The work on this project could involve:

  • To investigate effective sensing techniques to capture human motion features, using contact sensors, and collect real-world datasets. 
  • To design and apply efficient machine learning algorithms to recognize the motion features/intention and dynamically link such results with the rehabilitation robot. 
  • To apply the robot to the user for further improvement of rehabilitation.

In human-robot interaction/collaboration, the robot is supposed to be able to detect, perceive and understand corresponding human motions in the environment to interact, co-operate, imitate or learn in an intelligent manner. Robot鈥檚 perception intelligence through sensory systems plays a key role to make the robot interact with human in a more natural and efficient way. This project aims to design effective and efficient methods to sense human/patient motion and recognize the intention, and to real-time control a novel rehabilitation exoskeleton robot. They are expected to timely and correctly respond to user鈥檚 motion intention to assist the daily life and rehabilitation. 

This project goes well along with and contributes to our on-going EU funded project, 鈥淎iBle鈥 project (totalling 鈧5 million), which brings together cutting-edge technology in artificial intelligence, virtual reality, cloud computing and exoskeleton control. This robot-enhanced rehabilitation will help stroke patients make the best recovery possible and re-learn skills for everyday life. 黑料入口 is the lead partner of this project and will be responsible for the motion analysis and recognition using the multi-sensory information from the patients. 

Dr Zhaojie Ju, Professor in Machine Learning and Robotics, is the Principle Investigator of the "AiBle鈥 project and leading the consortium of 9 EU partners. He has published over 200 publications in journals, book chapters, and conference proceedings, and received 5 best paper awards, 1 book award, 1 best competition paper award, and 1 best Associate Editor award. He is an Associate Editor of top journals, such as IEEE Trans. Cybernetics, IEEE Trans. Cognitive and Developmental Systems, and Neurocomputing. He has successfully supervised over 10 PhD students, who found good jobs after graduation in either academic or industrial sectors. He is the Chair of IEEE SMC 黑料入口 Chapter and an IEEE Senior Member. 

Entry requirements

You'll need a good first degree from an internationally recognised university or a Master鈥檚 degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

You will need a first or upper second class honours degree or a good Masters degree in related area (such as: Engineering and Computer Science), and be able to demonstrate good problem solving and analytical skills as well as knowledge of artificial intelligence and robotics. Evidence of programming with Matlab and/or C/C++ is essential and experience in machine learning or robotics would be a significant advantage.

How to apply

We encourage you to contact Professor Zhaojie Ju (zhaojie.ju@port.ac.uk) to discuss your interest before you apply, quoting the project code below.

When you are ready to apply, please follow the 'Apply now' link on the Computing PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our 鈥How to Apply鈥 page offers further guidance on the PhD application process. 

When applying please quote project code: COMP6511025