Project code



School of Computing

Start dates

October, February and April

Closing date

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 Dr Zhaojie Ju.

The work on this project will:

  • Investigate effective representation of features for human arm and hand motions using machine learning algorithms, including the local and globe features, incorporating multi-sensory information i.e. surface electromyographic based muscle signals, contact force, joint torque, and accelerometer information.
  • Synchronise and fuse the sensory information for the real-time analysis and automatic recognition of the human actions with satisfactory accuracy and reliable fusion results. The priority is given to balancing the effectiveness and efficiency of the system.
  • Investigate effective AI based methods for recognising human arm and hand motions and validate them on a pre-designed rehabilitation exoskeleton robot. 

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. The extraction of meaningful information on human motions through sensory systems plays a key role in recognition to make the robot interact with human in a more natural and efficient way. This project aims to design effective and efficient methods to recognize human/patient motion intention to real-time control a novel rehabilitation exoskeleton robot. They are expected to correctly respond to user’s motion intention to assist their daily life and rehabilitation. 

This project goes well along with and contributes to our on-going EU funded project, “AiBle” project (totalling €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. University of Portsmouth 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, Reader in Machine Learning and Robotics, is the Principle Investigator of the "AiBle” 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 supervised over 10 PhD students, who found good jobs after graduation in either academic or industrial sectors. He is the Chair of IEEE SMC Portsmouth Chapter and an IEEE Senior Member.

Fees and funding

Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).

2022/2023 fees (applicable for October 2022, February and April 2023 start) 

PhD and MPhil

UK, Channel Islands and Isle of Man students 

  • Full-time: £4,596 (may be subject to annual increase)
  • Part-time and part-time distance learning: £2,298 (may be subject to annual increase)

EU students
(including Transition Scholarship)

  • Full-time: £4,596 (may be subject to annual increase)
  • Part-time and part-time distance learning: £2,298 (may be subject to annual increase)

International students

  • Full-time: £18,300 per year (may be subject to annual increase)
  • Part-time and part-time distance learning: £9,150 per year (may be subject to annual increase)

All fees are subject to annual increase. If you are an EU student starting a programme in 2022/23 please visit this page.

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.

Entry requirements

You'll need a good first degree from an internationally recognised university or a Master’s degree in an appropriate subject such as Engineering and Computer Science. 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 should 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’d encourage you to contact Dr Zhaojie Ju ( to discuss your interest before you apply, quoting the project code.

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 ‘How to Apply’ page offers further guidance on the PhD application process. 

When applying please quote project code: COMP5451021