Mechanics / Structural Vibration: Fully Funded PhD Studentship in Digital Approaches to Nonlinear Structural Dynamic Modelling

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. The result will be a scalable methodology that improves the performance of ROMs, making them more applicable to real-time structural health monitoring, vibration analysis, and control design. 

This research offers real-world impact across several industries, including: 

• Aerospace: Faster, more efficient design and real-time condition monitoring for safer aircraft and spacecraft. 
• Civil Engineering: Real-time health monitoring for bridges and skyscrapers, enabling early detection of damage. 
• Renewable Energy: Rapid, optimized design of wind turbine blades and structures for greener energy. 
• Microstructures: Accurate, efficient analysis of devices like MEMS, with minimal computational cost. 

By developing an advanced reduced order modelling framework, this project will empower engineers and designers to achieve more with less—delivering high-impact decisions quicker, with greater confidence, and at much lower cost.  

Funding: This scholarship covers the full cost of tuition fees and an annual stipend at UKRI rate (currently £20,780 for 2025/26) with a minimum enhanced stipend payment of £2000 per annum. Additional research expenses of up to £1,000 per year will also be available.  Funding Duration: 4 years 

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