EPSRC ICASE Studentship in learning based hybrid PDE solver

Users of Computer Aided Engineering applications always ask for higher computational speed and accuracy. Adopting Digital Twins broadly in the future, we expect this need to significantly increase. Recently, hybrid technologies - combining machine learning and classical simulation technologies - have been proposed to bring computational speed and accuracy of simulation tools to a new level. They thus have the potential to address user needs significantly better.

We invite applications from highly motivated individuals to join this project and contribute to this exciting area of research. 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, Machine learning, Applied Mathematics or related subject. This studentship is open to both home and overseas applicants. The successful candidate will work collaboratively with a multidisciplinary team based in Cambridge Universiy and Siemens Digital Industry Software, gaining expertise in advanced computational methods and state-of-the-art machine learning techniques.