Research Associate/Fellow (fixed-term)
Image analysis and machine learning approaches today are powerful tools allowing new ways of analysing and visualising biological data. Light sheet microscopy is a cutting-edge imaging approach which allows us to image plant cells in 3D and over time. Using this approach we can collect datasets showing size, shape and growth changes in plant cells over time. However, the datasets are very large and are not easy to visualise. Therefore, the motivation of this work is to build new image analysis and machine learning approaches which can allow biologists to identify interesting developmental events happening in these datasets. Events such as nuclei divisions, or cell shape changes over time need to be detected and localised in the data.
To do this will require development of algorithms and tools which make use both of traditional image analysis (such as segmentation and object tracking) and machine learning approaches, potentially including deep learning. It will require both the application of existing methods to these new datasets, and the development of new, exciting image analysis and machine learning approaches.
The candidate should have or nearly have a PhD in image analysis or an imaging-related machine learning subject. The ability to work in an interdisciplinary team will be essential. The candidate will be based at the University of Nottingham, and will be part of the Computer Vision Laboratory, http://www.nottingham.ac.uk/research/groups/cvl/index.aspx.
This position is based on a full time, fixed-term contract until 30 November 2019, however there may be the possibility of some extension after this. Applications for job share will also be accepted.
Informal enquiries may be addressed to Andrew French, andrew.p.french@nottingham.ac.uk . Please note that applications sent directly to this email address will not be accepted.
Further details:
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