The System Identification of Nonlinear Space Structures via Physics-Informed Machine Learning Scholarship 2026 at the University of Southampton, United Kingdom, is a fully funded doctoral research opportunity. The project develops advanced physics-informed machine learning (PIML) methods to model, identify, and predict the nonlinear dynamic behaviour of lightweight space structures used in modern spacecraft and satellite systems.

About the System Identification of Nonlinear Space Structures via Physics-Informed Machine Learning Scholarship

The University of Southampton offers this PhD under the Faculty of Engineering and Physical Sciences, within the Astronautics Research Group.

The research addresses a critical challenge in space engineering: lightweight structures used in spacecraft often exhibit nonlinear dynamic behaviour caused by friction, geometric nonlinearities, and contact effects. These behaviours cannot be accurately captured using traditional linear models.

The project develops physics-informed machine learning frameworks to improve system identification and build accurate reduced-order models of complex space structures.

The research focuses on:

  • Developing nonlinear system identification methods using physics-informed machine learning
  • Creating computationally efficient reduced-order models for space structures
  • Improving prediction accuracy for nonlinear vibration and structural response
  • Validating models using numerical simulations and experimental testing

The project combines:

  • Structural dynamics
  • Nonlinear vibration theory
  • Machine learning
  • Experimental mechanics

It also includes training in advanced modelling techniques and access to state-of-the-art experimental facilities for validating aerospace structures under realistic conditions.

Why Choose The System Identification of Nonlinear Space Structures via Physics-Informed Machine Learning Scholarship?

This PhD allows you to work at the intersection of space engineering, AI-driven modelling, and nonlinear dynamics.

You gain experience in:

  • Physics-informed machine learning (PIML)
  • Nonlinear system identification and modelling
  • Structural dynamics and vibration analysis
  • Experimental validation of aerospace structures

You also build strong expertise relevant to:

  • Spacecraft structural design
  • Aerospace simulation and digital twins
  • AI-driven engineering systems

System Identification of Nonlinear Space Structures via Physics-Informed Machine Learning Scholarship Summary

  • Host Country: United Kingdom
  • Host University: University of Southampton
  • Scholarship Name: System Identification of Nonlinear Space Structures via Physics-Informed Machine Learning
  • Study Level: PhD (Doctoral Research)
  • Funding Type: Fully funded studentship
  • Duration: Up to 3.5–4 years
  • Funding Coverage: Full tuition fees + UKRI stipend (tax-free)
  • Research Field: Aerospace engineering, AI, structural dynamics, machine learning
  • Selection Basis: Academic excellence in engineering, mathematics, or physics
  • Application Requirement: Direct PhD application with supervisor contact
  • Closing Date: 31 August 2026

Scholarship Benefits

This PhD studentship provides:

  • Full tuition fee coverage
  • A tax-free UKRI doctoral stipend for living expenses
  • Training in physics-informed machine learning and nonlinear dynamics
  • Access to advanced structural testing laboratories
  • Opportunities to publish in high-impact aerospace and AI journals
  • Collaboration with leading researchers in space systems engineering
  • Hands-on experience with experimental and computational validation

Eligibility Criteria

You qualify for this PhD if you:

  • Hold a UK 2:1 honours degree or international equivalent
  • Have a background in:
    • Aerospace engineering
    • Mechanical engineering
    • Physics
    • Applied mathematics
  • Demonstrate strong understanding of:
    • Structural dynamics
    • Vibration theory
    • Numerical modelling
  • Have (preferred):
    • Programming skills (Python, MATLAB, or similar)
    • Interest in machine learning or data-driven modelling

Required Documents

You must submit:

  • Completed the University of Southampton PhD application form
  • Academic CV
  • Degree transcripts and certificates
  • Two academic references
  • Personal statement or research proposal
  • English language test results (if required)
  • Supervisor contact details (recommended)

Application Process & Timeline

You follow these steps:

  1. You apply for a PhD in Engineering & Physical Sciences (Astronautics pathway) at Southampton.
  2. You contact the project supervisor (Dr Cristiano Martinelli / Prof Andrea Cammarano).
  3. You prepare your CV, transcripts, references, and research statement.
  4. You submit your application through the university portal.
  5. The university evaluates your academic and technical background.
  6. Shortlisted candidates may be interviewed.
  7. Successful applicants receive a fully funded PhD offer.