PhD in Engineering

Identifying and validating models for complex structures featuring nonlinearity remains a cutting-edge challenge in structural dynamics, with applications spanning civil structures, microelectronics, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics using machine learning to build robust, interpretable models from experimental and operational data. The core goal is to balance model accuracy with computational efficiency while meeting the needs of experimental validation. The framework will harness advanced techniques such as machine learning, optimization algorithms, and sensitivity analysis to automate and enhance the mode selection process. This research offers real-world impact across several industries, including aerospace, civil engineering, renewable energy, and microstructures. The position comes with substantial career development opportunities including funds for networking, research costs and training, and a placement at the Dyson Institute of at least 3 months.