
Dr Tom Hickling
MEng, DPhil
Expertise
Tom's research focuses on finding new ways to use machine learning and AI to accelerate development in aerospace engineering by improving modelling techniques, designing better geometries, and enabling AI-driven flow control.
Current positions
Lecturer in Engineering with AI
School of Civil, Aerospace and Design Engineering
Contact
Press and media
Many of our academics speak to the media as experts in their field of research. If you are a journalist, please contact the University’s Media and PR Team:
Biography
https://t-hickling.github.io/
Publications
Recent publications
01/05/2026Active Control of Turbulent Airfoil Flows Using Adjoint-Based Deep Learning
AIAA Journal
OGF: An online gradient flow method for optimizing the statistical steady-state time averages of unsteady turbulent flows
Journal of Computational Physics
Large Eddy Simulation of Airfoil Flows Using Adjoint-Trained Deep Learning Closure Models: 2024 AIAA SciTech Forum
AIAA SCITECH 2024 Forum
LES-CHT for a Rotating Cavity With Axial Throughflow
Journal of Turbomachinery