Research Assistant: AI-Enabled Inspection and Digital Twin for Critical Infrastructure (Transportation)

Location: City Centre Campus (Millennium Point)

Fixed Term contract for 24 months

We are seeking a Research Assistant: AI-Enabled Inspection and Digital Twin for Critical Infrastructure (Transportation) to join our Department of Architecture and the Built Environment, School of Architecture, Built Environment, Computing and Engineering (ABCE), contributing to internationally funded research developing AI-driven inspection and digital twin systems for transportation infrastructure.

In this role, you will play a central role in BCU’s contribution to the STRUCTURE project – a European project developing AI-enabled autonomous inspection and digital twin platforms for critical transportation infrastructure, including bridges, railways, and airport runways. Key responsibilities include AI model development for automated defect detection, sensor data fusion, and digital twin integration specific to transportation assets, working closely with a European consortium of universities, technology companies, infrastructure operators, and national highways authorities to ensure services are efficient, compliant, and aligned to University priorities.

Key Responsibilities

• Develop and apply AI and machine learning algorithms for automated defect detection and structural condition assessment of bridges using UAV-collected inspection data
• Process and analyse aerial inspection datasets from bridge structures, developing robust algorithms capable of identifying defects, deterioration, and structural anomalies
• Contribute to the development and validation of digital twin models for bridge assets, integrating AI-derived condition assessments for predictive maintenance and asset management applications
• Collaborate with consortium partners on the integration of AI analytics and digital twin outputs into operational inspection and maintenance workflows for bridge infrastructure owners and operators
• Produce peer-reviewed journal papers, conference presentations, technical reports, and dissemination materials aligned with STRUCTURE project obligations
• Engage with end-users and infrastructure operators to ensure AI and digital twin outputs are aligned with operational maintenance workflows and regulatory requirements

Essential Requirements

• A minimum 2:1 undergraduate degree in Computer Science, Artificial Intelligence, Software Engineering, Civil Engineering, Digital Built Environment, or a closely related discipline
• Experience with AI and machine learning techniques for image/sensor data analysis, computer vision, or structural/geospatial data processing
• Proficiency in Python and/or other relevant programming languages for data processing, model training, and integration
• Understanding of data management principles for structured and unstructured datasets from inspection or monitoring systems
• Strong written and verbal communication skills, with the ability to present technical findings to both academic and non-technical audiences
• Ability to work independently and collaboratively within an international multi-partner research consortium, managing time effectively to meet project milestones

Desirable Requirements

• MSc or PhD in Artificial Intelligence, Data Science, Digital Built Environment, Civil/Structural Engineering, or a closely related discipline
• Familiarity with UAV/drone inspection technologies, remote sensing, or non-destructive evaluation (NDE) methods applied to infrastructure
• Knowledge of digital twin platforms, Building Information Modelling (BIM), or asset lifecycle management systems
• Understanding of transportation infrastructure types (bridges, rail, road, airfield pavements) and their inspection and maintenance requirements
• Experience in an applied or industry-facing research environment such as a funded project, KTP, or industrial placement
• A track record of academic publication or evidence of research dissemination

For an informal discussion about the role, please contact Dr Saeed Talebi at Saeed.Talebi@bcu.ac.uk or Dr Vahid Javidroozi at Vahid.Javidroozi@bcu.ac.uk.  

Closing Date: 23.59 hours BST on Thursday 30 April 2026

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