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Nonlinear complex systems with application in biomedical systems

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  • Full or part time
    Dr L Guo
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

Biomedical engineering concerns the application of engineering principles and design concepts to medicine and biology for healthcare purposes. In silico modelling and computational simulations provide a valuable tool for the analysis of biomechanical behaviour and throughout the drug development process, from early target and hit identification to clinical trials. However, the multi-scale feature, unobservable/partially observable states and variables, highly complex and uncertainty pose great challenge to the modelling, analysis and prediction of such systems. A project in this theme can either focus on the algorithms for system identification, nonlinear modelling, time and frequency domain analysis, or applications in a range of topics in biomedical systems such as ground reaction force prediction, human movement analysis, muscle force prediction, joint contact force computations, and bone remodelling study. The specific focus of the project will be agreed in discussion between the supervisor and student.

Prospective candidates with background in Engineering, Physics, Mathematics, Biomedical and Bioengineering, or related disciplines, with good academic performance records in their undergraduate/postgraduate studies, are encouraged and invited to apply for and get involved in this project.

Funding Notes

Applicants can apply for a Scholarship from the University of Sheffield but should note that competition for these Scholarships is highly competitive. It will be possible to make Scholarship applications from the Autumn with a strict deadline in late January/early February. Specific information will appear:

How good is research at University of Sheffield in General Engineering?

FTE Category A staff submitted: 21.80

Research output data provided by the Research Excellence Framework (REF)

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