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Machine learning and dynamics: Soft materials under stress


About the Project

The PhD, which is a collaboration with Bayer CropScience labs in Mannheim, Germany, will use laser sheetmicroscopy and machine learning to predict the dynamics of soft materials under a load. This will interest applicants who want to develop expertise in the application of machine learning to the physical sciences. Previous work at Bristol has shown that soft materials when loaded display a very rich response, with randomly distributed local precursor events growing in extent with time, which eventually coalesce into an avalanche signalling failure of the system. Using a novel optical system, we will image these changes and then develop machine learning algorithms to identify precursor regions, and so predict their time evolution. An innovative aspect of the project is the development of machine learning algorithms to collect and analyse the large amount of image data generated, with the ultimate aim of predicting material failure. The student will spend the majority of their time at Bristol but will be encouraged to visit Bayer’s labs for specialist rheology measurements (3 months). For more information please email .

Candidate requirements:
Good 1st degree in Chemistry/Physics/Engineering

How to apply:
Please make an online application for this project at Please select Chemistry (PhD) on the Programme Choice page. You will be prompted to enter details of the studentship in the Funding and Research Details sections of the form.

Prospective candidates are encouraged to apply as soon as possible. This advert will be taken down before the stated deadline should a suitable candidate be found.

Funding Notes

This is an IILF iCASE joint funded studentship from EPSRC and Industry (Bayer). The studentship covers fees and provides a stipend for 3.5 years.

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