Mechanical properties of materials are usually measured by simple one-dimensional tests. The growing field of data-driven mechanics requires development of experimental methods to obtain large quantities of multi-axial data from a single test. To complement this data is the requirement to develop computational methods that can deal with the inevitable measurement noise. We are starting a new project with the aim to use: (i) lab-based flux enhanced tomography for full field measurement of deformation fields and X-ray diffraction measurements of elastic strains, and (ii) associated data-driven material model discovery techniques. These coupled measurements and machine learning techniques are expected to form an important element in the field of data-driven mechanics. We are looking for a PhD student to join the project to work alongside post-doctoral associates and our partner universities in the US.

Applicants should have (or expect to obtain by the start date) at least a high 2.1 degree (preferably a first or its equivalent) in Engineering, Physics or related subject. A strong interest in multi-physics modelling and/or experimental methods is essential. This studentship is open to both home and overseas applicants.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.