Research Fellow in Scientific Computing

Are you an ambitious researcher looking for your next challenge?

Do you have a background in scientific computing/ computational science & engineering with an interest in data-driven algorithms?

There is a rapid growth in interest in the use of data-driven and machine-learning (ML) algorithms in the fields of scientific computing and computational science & engineering. The purpose of this research is to investigate ways to enhance traditional numerical algorithms based upon recent advances in data-driven algorithms. In particular, the goal is to avoid black-box approaches by building physical models into the ML models that are used. Outcomes will include both published research and submitted applications for external funding in order to extend the scope of the research further.

With a PhD (or close to completion) in Computer Science, Engineering, Mathematics or an allied discipline, you will have a strong background in scientific computing or computational science & engineering, preferably relating to the solution of problems in continuum mechanics (fluids, solids or both).

Professor Peter Jimack , Professor of Scientific Computing