About this project
This PhD project will combine the state of the art in biologically inspired computational architectures with more classical machine learning techniques to try and understand and predict human movement. In particular this work will focus on the decomposition of complex tasks into smaller and simpler sub-tasks to improve the transparency of machine learning decision making. This is a process which biologically inspired computational architectures such as artificial epigenetic networks are particularly adept at. Human movement is a very complex process, which is often difficult to understand and model within a 'real world' setting, and this work will look at building a network of low-power wearable sensors in order to best improve this understanding. Within this work, we see one of the key applications to be, but not limited to the analytics of sports movement. Another area of application includes using wearable sensors in conjunction with smart phone technology to provide personalised medical diagnostics.
Lead Supervisor: Dr Alexander Turner, email@example.com
Prof Ken Hawick
Dr Nina Dethlefs
Full-time UK/EU PhD Scholarships will include fees at the ‘home/EU' student rate and maintenance (£14,121 in 2016/17) for three years, depending on satisfactory progress.
Full-time International Fee PhD Studentships will include full fees at the International student rate for three years, dependent on satisfactory progress.
Candidates should have excellent programming skills and a degree in Computer Science or a related discipline. Experience in machine learning is essential. Further knowledge and expertise of one or more of the following areas is highly desirable: evolutionary algorithms, deep learning, natural language processing, embedded systems and the Internet of Things (IoT).
Successful applicants will be informed of the award as soon as possible and by 8th May 2017 at the latest.
Find out more about research in Computer Science.