About the project
This project aims to develop advanced digital twin technology for space object re-entry monitoring, addressing multiphysics coupling and hybrid uncertainties. The project focuses on uncertainty quantification, robust model updating, and real-time data integration to improve re-entry prediction accuracy.
This project addresses the growing challenge of uncontrolled space debris re-entry, posing risks to residential areas and Earth’s sustainability. The research aims to revolutionize space object re-entry monitoring by developing a robust digital twin system that combines advanced uncertainty quantification, real-time data integration, and efficient multiphysics simulation.
Key objectives include:
- creating hybrid aleatory (random) and epistemic (unknown-but-fixed) uncertainty models to accurately predict re-entry outcomes
- developing highly efficient data-driven surrogate models to balance computational efficiency and precision
- building a digital twin capable of real-time data assimilation and bidirectional interaction for re-entry trajectory monitoring
- providing robust predictions of space debris re-entry trajectories and impact regions, accounting for multi-source and mixed uncertainties
You will gain expertise in cutting-edge techniques like stochastic model updating, Bayesian inference, and digital twin technology. You will work on a unique combination of forward uncertainty propagation and inverse model updating methods, contributing to the next generation of aerospace monitoring systems.
The project is based in our Department of Aeronautics and Astronautics, a leading institution with a strong track record in aerospace research. You will collaborate with experts from academia and industry, gaining access to advanced facilities and a multidisciplinary research environment.
Your work will have a profound impact on enhancing the safety and sustainability of space exploration, setting the foundation for better risk assessment and mitigation of space debris.
You will contribute to an innovative project with real-world applications and global significance.
Entry requirements
A UK 2:1 honours degree, or its international equivalent, in one of the following:
- aerospace engineering
- mechanical engineering
- applied mathematics
or a related field.
Essential skills:
- knowledge of finite element analysis (FEA) or computational fluid dynamics (CFD)
- proficiency in programming languages such as Python, MATLAB, or similar tools
- strong written and verbal communication skills, with the ability to present technical information effectively
Desirable skills:
- experience with uncertainty quantification techniques, stochastic modelling, or Bayesian inference
- familiarity with digital twin frameworks or multiphysics simulation tools
- prior research experience, such as a Master’s thesis, in aerospace or related disciplines
- good analytical and problem-solving skills, with an ability to tackle complex engineering challenges
Fees and funding
For UK students, tuition fees will be paid and you’ll receive a stipend (living allowance) at the EPSRC rate (approximately £1,600 per month).
Please note, there is a gap between the tuition fee for UK students and that for international students, which is approximately £20,288 per year. While it is possible to cover this differential fee through competitive funding at the University of Southampton, international applicants should be prepared to cover this difference through self-funding if necessary.
We also offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.
Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.
Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs (minimum of £19,237 in 2024-25) for top-ranked applicants.
Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.
How to apply
You need to:
- choose programme type (Research), 2025/26, Faculty of Engineering and Physical Sciences
- select Full time or Part time
- choose the relevant PhD in Engineering
- add name of the supervisor in section 2
Applications should include:
- personal statement
- your CV (resumé)
- 2 academic references
- degree transcripts to date
Contact us
Faculty of Engineering and Physical Sciences
If you have a general question, email our doctoral college (feps-pgr-apply@soton.ac.uk).
Project leader
For an initial conversation, email Dr Sifeng Bi (Sifeng.Bi@soton.ac.uk).
