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Tracking and Modelling of Cell Dynamics with Application to Swarm Robots

Project Description

Understanding the dynamics of cell behaviour has the potential to lead to identifying the mechanisms of diseases such as cancer and of immune function. The increasingly sophisticated measurement techniques in life science has given rise to high resolution high throughput screening and in-vivo time lapse image sequences. Such image sequences contain single cell and population level information. Changes in patterns of cell behaviour can be associated with potential effectiveness of a therapeutic drug.

The aims of a number of related projects is to analyse such image sequences and extract cell motion / shape characteristics and their change over time for individual cells and across the cell population. It will require tracking the cell motion and characterising their behaviour patterns. Additionally, the cell shape features will be identified and associated with cell motion. Correlations between motion patterns and cell shapes will be analysed and quantified. An important aspect will be to quantitatively assess changes in cell behaviour under different drug environments. This understanding will then be incorporated into swarm robots for suitable applications. The projects are carried out in collaboration with University of Sheffield Departments in relevant application areas and other leading UK Institutions.
The projects will require candidates to have excellent mathematical and computational skills. It will be an advantage if the candidates have familiarity with signal processing methods such as target tracking, signal interpolation, model-based methods.

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: View Website

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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