rules by which different recurrent patterns can coexist and interact and it is a robust platform on which to build low-order models for deployment in industrial applications. However, to date this approach has been restricted to small-scale flows because of (1) the computational challenge of identifying the coherent patterns and (2) the combinatorial challenge of identifying the rules by which the patterns join to form the space-time tapestry. These problems are a natural fit for frontier AI models. The two postdocs on this project will, together with the PI, his collaborators and industrial partners, design, train and release these models.

These positions are funded by UKRI to work with Dr Jacob Page on the design and construction of large neural networks for the embedding and prediction of high Reynolds number turbulence. The approach is motivated by Hopf’s “dynamical systems” view of turbulent flows. The project includes collaboration with a leading researcher at a major tech company, in addition to ongoing collaborations with Prof Steve Brunton’s group at the University of Washington and Dr Georgios Rigas’ group at Imperial College London. There will also be scope for involvement in other ongoing industrial collaborations.

£41,064 to £48,822 per annum (Grade 7)

rules by which different recurrent patterns can coexist and interact and it is a robust platform on which to build low-order models for deployment in industrial applications. However, to date this approach has been restricted to small-scale flows because of (1) the computational challenge of identifying the coherent patterns and (2) the combinatorial challenge of identifying the rules by which the patterns join to form the space-time tapestry. These problems are a natural fit for frontier AI models. The two postdocs on this project will, together with the PI, his collaborators and industrial partners, design, train and release these models.

These positions are funded by UKRI to work with Dr Jacob Page on the design and construction of large neural networks for the embedding and prediction of high Reynolds number turbulence. The approach is motivated by Hopf’s “dynamical systems” view of turbulent flows. The project includes collaboration with a leading researcher at a major tech company, in addition to ongoing collaborations with Prof Steve Brunton’s group at the University of Washington and Dr Georgios Rigas’ group at Imperial College London. There will also be scope for involvement in other ongoing industrial collaborations.

£41,064 to £48,822 per annum (Grade 7)