Embedded Intelligence for Pro-active Maintenance of Infrastructure Assets (Ref: SF-KK-2025/2)

About the Project

We are seeking a highly motivated and talented individual to join the Wolfson School of Mechanical, Electrical and Manufacturing Engineering as a PhD researcher. This PhD project focuses on developing machine learning (ML) techniques running on constrained edge devices, such as Uncrewed Aerial Vehicles (UAVs), for identifying construction defects in buildings and infrastructure assets. Research challenges in this cross-domain area include the speed of processing that is required in low-cost and resource-constrained devices, as well as handling the uncertainty in image classification results.

The successful candidate will be responsible for developing and implementing ML algorithms capable of performing on-device classification, rather than transmitting images to a base station for off-line processing. The aim is to accurately and effectively quantify the remaining useful life, vulnerability and risk of failure, due to corrosion, fatigue, and material degradation, of infrastructure assets. Quantifying and expressing the level of uncertainty in image classification from UAV cameras will be an integral part of the research. Bayesian deep learning and Evidence Theory frameworks will be investigated and implemented to advance current state-of-the-art approaches in life-cycle and risk management of infrastructure, enabling uncertainty-aware decision-making for inspection and maintenance planning The project will explore lightweight embedded AI techniques (e.g. quantisation, pruning and knowledge distillation) for deployment on UAV-class hardware.

The ideal candidate should have experience in machine learning, computer vision or data science, and an interest in structural health monitoring, or manufacturing/inspection processes. Experience with supervised and unsupervised learning techniques and programming languages such as Python or Matlab is highly desirable.

The successful candidate will have the opportunity to work with leading researchers in the field and to present their work at international conferences and publish in high-impact journals. Furthermore, there will be an opportunity to attend summer schools and Continuing Professional Development courses. The position has a flexible starting time.

The student will work with Dr Konstantinos Kyriakopoulos (ML, decision making under uncertainty), Dr Konstantinos Baxevanakis (Mechanics of Materials, Nondestructive Evaluation methods) and Sam Amiri (microelectronics, embedded systems and edge computing). To apply, please submit your CV, a cover letter, and contact details of two referees. Please contact the primary supervisor, Dr Kostas Kyriakopoulos for more details and for raising your interest.

We particularly encourage applications from individuals eligible for national scholarship funding such as CSC (Chinese Scholarship Council) funding or Commonwealth Scholarships.

Name of primary supervisor/CDT lead:

Kostas Kyriakopoulos elkk@lboro.ac.uk

Names of secondary supervisors:

Kostas Baxevanakis and Sam Amiri

Entry requirements:

Applicants should have or expect to achieve at least a 2:1 honours degree (or equivalent) in Computer Science, Mechanical Engineering, Materials, Aerospace Engineering or a related subject. A relevant Master’s degree and/or experience in one of these areas will be an advantage. Strong research abilities with appropriate coding skills are required. The successful candidate is also expected to be an enthusiastic team player who can work both independently and communicate effectively with others.

English language requirements:

Applicants must meet the minimum English language requirements.

Bench fees required: No

Closing date of advert: 15 June 2026

Start date: 01 October 2026

Full-time/part-time availability: Full-time 3 years

Fee band: 2025/26 Band RB (UK £5,006, International £28,600)

How to Apply:

All applications should be made online. Under Campus, please select ‘Loughborough’ and select Programme ‘Electronic and Electrical Engineering’. Please quote the advertised reference number SF-KK-2025/2 in your application under the ‘Finance’ section.

Applications must include a personal statement, up-to-date curriculum vitae (CV), details of two referees (one from your highest degree qualification), certified certificates and transcripts for all completed degree programmes, and a reference to the project ‘SF-KK-2025/2’.

To avoid delays in processing your application, please ensure that you submit the above supporting documents.

Project search terms: artificial intelligence, built environment, computer architectures, machine learning, defect identification

Email address Wolfson: ws.phdadmin@lboro.ac.uk