Digital Twin Extrapolation in Space Object Re-Entry Monitoring

Project Summary

Develop advanced digital twin technology for space object re-entry monitoring, addressing multiphysics coupling and hybrid uncertainties. This PhD focuses on uncertainty quantification, robust model updating, and real-time data integration to improve re-entry prediction accuracy. Collaborate with leading experts and industry partners on this cutting-edge aerospace research opportunity.

Project Details

This PhD 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.

As a PhD candidate, 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 the Department of Aeronautics and Astronautics at the University of Southampton, 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. 

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Essential Skills:

• A strong academic background in Aerospace Engineering, Mechanical Engineering, Applied Mathematics, or a related field.
• 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 modeling, 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.

Closing date: 31 August 2025. 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. For more information please visit PhD Scholarships nearmejobs.eu Doctoral College nearmejobs.eu University of Southampton Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.

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 Sifeng Bi

Applications should include:

• Research Proposal
• Curriculum Vitae
• Two reference letters
• Degree Transcripts/Certificates to date

For further application information please contact: [email protected]

If you would like to discuss the project details before applying, please contact Dr. Sifeng Bi directly at sifeng.bi(at)soton.ac.uk.