Nonlinear Dynamics/Vibrations: System Identification of Nonlinear Space Structures via Physics-Informed Machine Learning

About the Project

Supervisory Team: Dr Cristiano Martinelli and Prof Andrea Cammarano

Modern lightweight space structures face harsh environments and often exhibit nonlinear dynamics due to contacts, friction, and geometric nonlinearities. This PhD project combines numerical, analytical, and experimental methods to develop physics-informed machine learning tools for efficient nonlinear system identification, enabling accurate modelling and validation of the next-generation space technologies.

Modern space systems - from spacecraft components to precision sensors - operate in extreme and hostile environments. To meet stringent performance demands while minimising payload mass, ultra-lightweight high-performance structures are increasingly employed in space missions. Although such advanced structures offer exceptional capabilities, they often exhibit nonlinear dynamic behaviours which cannot be captured by employing classical linear models. Such behaviours arise from geometric nonlinearities, friction, contact, and complex damping mechanisms, all of which critically impact the performance, stability, and reliability of space structures. In this context, developing novel tools for the analysis, identification, and prediction of the dynamics of nonlinear systems is essential for designing, testing, and validating the next generation of space technologies.

This PhD project will combine numerical modelling, advanced analytical techniques, and experimental methods to develop a novel approach for the identification of nonlinear systems. Specifically, the PhD project aims to develop a novel nonlinear system identification method based on physics-informed machine learning approaches, capable of producing computationally efficient reduced-order models and enabling accurate, efficient modelling of complex space structures. Investigate the accuracy and extrapolation capabilities of the identified reduced-order models, identifying pros and cons of the proposed approach. Validate theoretical and numerical results through state-of-the-art experimental facilities.

This project is ideal for candidates passionate about space engineering, nonlinear dynamics, and aerospace innovation, and will prepare graduates for high-impact careers in aerospace industries, research institutions, and academia.

If you wish to discuss any details of the project informally, please contact: Dr. Cristiano Martinelli at c.martinelli@soton.ac.uk.

The successful candidate will receive comprehensive training in structural dynamics, nonlinear dynamics, and system identification methods, as well as professional development in scientific writing, mentoring, and Equality, Diversity, and Inclusion (EDI) awareness. Opportunities include publishing in high-quality international journals, presenting at prestigious international conferences, engaging in collaborative research with leading industrial and institutional partners, and access to state-of-the-art experimental facilities.

Entry Requirements

• A UK 2:1 honours degree (or equivalent international qualification) in Aerospace Engineering, Mechanical Engineering, or a closely related discipline.
• Solid understanding of structural dynamics and vibration theory.
• Background in numerical modelling and/or experimental mechanics.
• Strong analytical abilities, with the capacity to work independently as well as collaboratively within a research team.
• Prior experience in nonlinear dynamics is advantageous.

Closing date: 31 August 2026. Applications will be considered in the order that they are received, the position will be considered filled when a suitable candidate has been identified.

Funding: We offer a range of funding opportunities for both UK and international students, including Bursaries and Scholarships.

How To Apply

Apply online: Search for a Postgraduate Programme of Study (soton.ac.uk) Select programme type (Research), 2025/26, Faculty of Engineering and Physical Sciences, next page select “PhD Eng & Env (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Dr Cristiano Martinelli

Applications should include: Research Proposal, Curriculum Vitae, Two reference letters, Degree Transcripts/Certificates to date

For further information please contact: feps-pgr-apply@soton.ac.uk