We are looking for PhD students, UROP and Erasmus+ students interested in data assimilation and scientific machine learning for fluids.

Openings and how to apply

PhD Students

NameDegreeThesis/Project titleYear
Ben EzePhD
Aeronautics, Imperial
Interpretable scientific machine learning for real-time prediction of chaotic systems
Funding President's PhD Scholarship
2026–

Master & Undergraduate Students

Current

NameDegreeThesis/Project titleYear
Lukas CampbellUROP
Aeronautics, Imperial
Autoencoder-based reduced order modelling2026
Tarek ObegiUG Summer project
Aeronautics, Imperial
Bidirectional digital twins for the control of chaotic systems2026
Konstantinos BarmpounisMSc Advanced Aeronautical Engineering
Aeronautics, Imperial
Real-time bias-aware data assimilation for turbulent wind farm wake estimation
Co-supervisor Luca Magri
2026
Salvatore AlojMSc Advanced Aeronautical Engineering
Aeronautics, Imperial
Wake reconstruction from wind turbine load measurements using a nonlinear machine learning approach
Co-supervisor George Rigas
2026

Past

NameDegreeThesis/Project titleYear
Ben EzeMEng Aeronautical Engineering (FYP)
Aeronautics, Imperial
Vision transformer masked autoencoders for restricted-domain flow reconstruction
Co-supervisor Luca Magri
2025-26
Josh CoskunMSc Advanced Aeronautical Engineering
Aeronautics, Imperial
Real-time data assimilation and modelling from particle image velocimetry measurements
Co-supervisor Luca Magri (main)
2025
Leo HsiehMEng Aeronautical Engineering (FYP)
Aeronautics, Imperial
Real-time prediction of chaotic systems' states and parameters
Co-supervisor Luca Magri (main)
2024-25