As part of the Solid Mechanics Research Group (SMRG) at University of Bristol, this role is to deliver research activities on deep learning artificial intelligence to develop surrogate models of complex microstructurally-informed metallic materials models. This role is being advertised as part of an exciting new five year project, called the SINDRI Partnership, funded by the Engineering and Physical Sciences Research Council (EPSRC), and EDF.
What will I be doing
You will – in collaboration with internal and external colleagues – identify, collect, and assimilate data from micromechanical experiments to effectively calibrate available physically based models of mechanical behaviour at meso-scale. You will also develop surrogate models/emulators based on microstructurally informed meso-scale materials models (provided by other project researchers). In addition, you will benchmark the developed surrogate reduced models against the performance of meso-scale materials models including quantify the uncertainty associated with each. You will collaborate with a wide range of academic and industrial partners; the main collaborating organisation will be EDF Energy – the company which operates the UK’s nuclear power stations. You will deliver academic impact from your work by publishing journal papers and attending conferences, while you will help deliver industrial impact by communicating your results to industrial collaborators.
You should apply if
You will have a PhD in Mechanical Engineering, Materials Science, mathematics, physics, electrical engineering. You will have experience of developing surrogate models/emulators and are familiar with relevant data models and optimisation techniques (e.g. Gaussian processes and Bayesian optimisation).