Tim Tang's Group Website

Tim Tang joined the University of Manchester in 2025 as a Lecturer (Assistant Professor) in Fluids Simulation and Digital Twins. Previously, he worked as a Course Director of the Intelligent Earth CDT programme and also as an Eric and Wendy Schmidt AI in Science Postdoctoral Fellow at the University of Oxford. He graduated with a BEng from the University of Nottingham. He moved straight to a DPhil at Oxford, publishing 5 journal papers, including three JFMs. He won first place in the Osborne Reynolds Day competition for the UK's best DPhil student in fluid mechanics.

His current research focuses on extreme events in fluid mechanics with an emphasis on machine learning, including data-driven predictions on extreme waves and extreme structural loading, considering leading order physics such as nonlinear wave dynamics and instabilities, breaking waves, and long-short wave interaction. His recent research on Rogue Wave has been showcased in BBC Science Focus.

This research group utilises cutting-edge machine learning to revolutionise our understanding of fluid mechanics. The group has a special interest in understanding the oceanic environment and its interaction with infrastructures. We seek to discover, understand, and model these nonlinear hydrodynamics with machine learning and apply them to marine energy as well as other classic fluid mechanical challenges such as turbulence modelling. 

I collaborate with worldwide researchers, and the University of Oxford is my “second academic home”, where I am a visiting academic in the Waves and Flows Research Group in the Department of Engineering Science.

My research team utilises a wide range of methodologies for fluid mechanics challenges, including field data analysis, fluid simulation, mathematical analytics, experiments, and state-of-the-art customised machine learning methods. This includes: Physics-Informed Neural Networks (PINN), Symbolic Regression, Gaussian Process, Random Forests, and later Vision Transformer.

My research team has a keen interest in scientific machine learning for fluid mechanics, in particular:

Knowledge Discovery with Machine Learning

Physics-Informed Reduced Order Modelling

My research team is working on a wide range of fluid mechanics problems with machine learning methods, including:

Extreme Wave and Wave Statistics

Wave-Structure Interaction

Wave Breaking

Oceanic Wave Environmental Forecasting

Coastal Hydrodynamics

Marine Energy

As Co-Supervisor with Samuel Draycott, Benedict Rogers and Alex Skillen, University of Manchester

• Chengyuan Hao, PhD research supervision (2025-) Accelerating offshore renewable energy deployment through AI models of the ocean

As Co-Supervisor with Thomas Adcock, University of Oxford (Department of Engineering Science):

• Jialun (Eric) Chen, AI mentor (co-supervisor) for Fellowship, (2024-) Next-generation prediction model for ocean science and renewables
• Xinyu Liu, DPhil research supervision (2023-) Nonlinear wave mechanics and rogue waves
• Thomas Monahan, DPhil research supervision (2022-2025) Spatiotemporal tidal prediction and analysis through physics-informed ML
• Douglas Brierley, Undergraduate research supervision (2022-2023) Machine learning in Tidal Energy Predictions
• Zhenhao Li, MSc research supervision (2021-2023) Numerical simulations of waves passing an abrupt depth transition

As Co-Supervisor with Moritz Reide University of Oxford (Department of Physics):

• Tobias Antippas, MSc Energy Systems Dissertation (2021-2022) Technical and economic intersection of floating solar PV technology with silicon and organic PV technology in offshore applications.

I am actively looking for PhD students (with fully funded studentship(s) avaliable) and also very happy to supervise and host Postdoctoral Fellowships. The best way of contacting me for more details is via email: tim.tang[at]manchester.ac.uk

Journal Reviewer

Physical Review Letters, Journal of Fluid Mechanics, Journal of Hydrology, Scientific Reports, Ocean Engineering, Journal of Ocean Engineering and Science, Wave Motion, Ocean Modelling, International Journal of Naval Architecture and Ocean Engineering, Mechanics and Mechanical Engineering, International Journal of Offshore and Polar Engineering (IJOPE) 

• Modelling & Simulation 2 [AERO20062]
• Fluid Mechanics 2    [AERO20121]