Please note: The scholarship covers Home tuition fees only. Applicants classified as international must demonstrate the ability to fund the difference between Home and International tuition fees. Applications from international candidates will be considered only where clear evidence of this funding is provided.
Description:
Are you interested in using data, sensors, and Artificial Intelligence to solve real engineering problems?
We are offering an exciting PhD opportunity at the University of Strathclyde on the development of new smart monitoring systems for infrastructure such as bridges, wind turbines, offshore structures, and transport assets. This project is ideal for students who enjoy engineering, mathematics, computing, or data analysis and want to work on research with clear real-world impact.
Why this project matters
Structures such as bridges, towers, wind turbines, and offshore platforms are expected to operate safely for many years, often in harsh and changing environments. They face traffic loads, wind, waves, temperature changes, ageing, and damage over time. Because of this, it is not always easy to know when a structure is performing well and when it may need attention.
Modern monitoring systems use sensors to measure how structures move and vibrate. These data can tell us a lot, but in practice the signals are often noisy, conditions keep changing, and traditional analysis methods do not always work well outside controlled settings.
This PhD will explore a new way of monitoring structures using adaptive Artificial Intelligence. Instead of using one fixed method from start to finish, the system will learn how to choose the most suitable analysis approach as conditions change. The aim is to create smarter monitoring tools that can better support safety, maintenance, and engineering decision-making.
What the project is about
This research will develop intelligent monitoring tools that can interpret data collected from real structures. The project combines ideas from structural engineering, vibration analysis, signal processing, probability, and AI.
In simple terms, you will work on methods that help answer questions such as:
- How can we use sensor data to understand whether a structure is behaving normally?
- How can we detect important changes when the data are noisy or conditions are changing?
- How can AI help choose the best analysis method automatically?
- How can we make these decisions in a reliable way when there is uncertainty?
The long-term goal is to create monitoring systems that are not only automated, but also trustworthy and adaptable in real engineering environments.
As the PhD student, you will be working at the intersection of AI, structural dynamics, sensing, data analysis, and infrastructure safety. You will develop new algorithms and tools for smart infrastructure monitoring and your work may include:
- Developing methods to extract useful information from vibration and sensor data
- Using probabilistic and Bayesian models to deal with uncertainty in the data
- Designing AI-based systems that can adapt and choose between different analysis methods
- Testing the methods using simulations, benchmark datasets, and laboratory or controlled experiments
Who should apply
We welcome applications from motivated students with a strong degree in subjects such as:
- Civil Engineering
- Mechanical Engineering
- Aerospace Engineering
- Mathematics
- Physics
- Computer Science
- Data Science
- Statistics
- or a related discipline
You do not need prior knowledge of Structural Health Monitoring before starting. What matters most is that you are curious, analytical, and interested in solving complex problems. Experience in coding, modelling, data analysis, or mathematics would be helpful, but you will receive training during the PhD.
Why this PhD is a great opportunity
This project will give you the chance to work on a highly relevant research area with strong links to the future of intelligent infrastructure and digital engineering. You will develop skills in:
- Artificial Intelligence and machine learning
- Sensor data analysis
- Signal processing
- Probabilistic modelling
- Scientific computing
- Infrastructure safety and resilience
These skills are valuable in both academia and industry, including careers in engineering consultancy, infrastructure management, energy, transport, digital twins, and advanced monitoring technologies.
Why Strathclyde
The University of Strathclyde offers a strong and supportive research environment with international expertise in risk, uncertainty, intelligent infrastructure, and digital engineering. You will join an active research group and work on a project with both scientific depth and clear practical importance.
This is an opportunity to contribute to safer, smarter, and more resilient infrastructure while building a powerful set of technical skills for your future career.
How to Apply
To apply, please contact Dr. Basuraj Bhowmik (basuraj.bhowmik@strath.ac.uk) and Prof. Edoardo Patelli (edoardo.patelli@strath.ac.uk) with the subject line “Smart Monitoring PhD Position”. Applications are welcome throughout the year. The position will remain open until a suitable candidate is appointed. As a part of the application pack, please include:
- A cover letter describing motivation and research interests
- An up-to-date CV
- Academic transcripts and certificates
Only shortlisted candidates will be invited for online interviews.
Funding Notes
The scholarship covers Home tuition fees only. Applicants classified as international must demonstrate the ability to fund the difference between Home and International tuition fees. Applications from international candidates will be considered only where clear evidence of this funding is provided.
To be classed as Home student, applicants must meet at least one of the following:
- Be a UK national meeting residency requirements
- Hold settled status in the UK
- Hold pre-settled status meeting residency requirements
- Have indefinite leave-to-remain or enter
International applicants are welcome but must cover the fee difference through self-funding or external support.