This interdisciplinary PhD research, conducted at Cranfield University, with focus on developing pre-emptive strategies informed by advanced predictive analytics to enhance the resilience of highly networked dynamic systems. By leveraging the Machine Learning-Based Bayesian-Optimized LightGBM Model, the project aims to proactively mitigate risks and facilitate positive restoration, contributing to the regeneration of evolving Cyber-Physical Systems (CPS) for sustainable growth and innovation. Through access to resources, expertise, and training opportunities, the student will gain transferable skills, expand their professional network, and enhance their employability prospects while making significant academic and practical contributions to the field.

This PhD project sits at the intersection of predictive analytics and system resilience, addressing the pressing need for pre-emptive strategies to mitigate risks and facilitate positive restoration.  

The aim is to develop and implement pre-emptive strategies informed by advanced predictive analytics for regeneration of evolving CPS for sustainable growth and innovation. 

Cranfield University provides an ideal environment for this interdisciplinary research, with its renowned expertise in areas such as predictive analytics, system resilience, and cyber-physical systems.  

The insights gained from this research can inform policy-making and decision-making processes in various domains, leading to more robust and resilient systems. 

There will be collaboration opportunities with world-leading experts within the International Systems Realization Partnership. 

Through this experience, the student will gain a wide range of transferable skills such as predictive analytics, system resilience, and cyber-physical systems, as well as critical thinking, problem-solving, and project management. 

At a glance

  • Application deadline27 Nov 2024
  • Award type(s)PhD
  • Start date27 Jan 2025
  • Duration of award3 years
  • EligibilityUK, Rest of world
  • Reference numberSATM484

Entry requirements

Applicants should have a first or second class UK honours degree or equivalent in data science, engineering, computer science, sustainability, and systems analysis. It welcomes applications from individuals passionate about addressing real-world challenges, in an inclusive and collaborative research environment.



Diversity and Inclusion at Cranfield 

At Cranfield, we value our diverse staff and student community and maintain a culture where everyone can work and study together harmoniously with dignity and respect. This is reflected in our University values of ambition, impact, respect and community. We welcome students and staff from all backgrounds from over 100 countries and support our staff and students to realise their full potential, from academic achievement to mental and physical wellbeing. We are committed to progressing the diversity and inclusion agenda, for example; gender diversity in Science, Technology, Engineering and Mathematics (STEM) through our Athena SWAN Bronze award and action plan, we are members of the Women’s Engineering Society (WES) and Working Families, and sponsors of International Women in Engineering Day. We are also Disability Confident Level 1 Employers and members of the Business Disability Forum.


This is a self-funded project, any interested applicants or any potential sponsor (local government or industry) will need to provide their own financial support in relation to tuition fees, research support fees and living expenses.

About the sponsor


Cranfield Doctoral Network

Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.

How to apply

For further information please contact: 

If you are eligible to apply for this studentship, please complete the online application form.